7024473	Knowl. Acquis.	5	\N	\N	June	\N	\N	\N	\N	\N	Academic Press Ltd.	London, UK, UK	\N	\N	Gruber93	http://dx.doi.org/10.1006/knac.1993.1008	\N		\N	\N	199--220	\N	2	\N	posted-at = {2005-09-17 15:18:31}, issn = {1042-8143}, citeulike-article-id = {244074}, priority = {3}, doi = {http://dx.doi.org/10.1006/knac.1993.1008}	\N	a18748a7f5e0fe51bb1fd562619601d2	232576339f9eecc6915dec6a2ee77150	68f6dc6852686794589bf5e2a9151cd3	article	A translation approach to portable ontology specifications	Thomas R. Gruber	\N	1993
7028163	\N	\N	\N	\N	\N	\N	Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops	\N	\N	\N	Springer	\N	\N	\N	christiaens06	http://www.springerlink.com/content/m370107220473394	\N		\N	\N	\N	\N	\N	\N	pdf = {christiaens06-metadata.pdf}, lastname = {Christiaens}, lastdatemodified = {2007-01-04}, read = {notread}, own = {notown}	In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.	eed329cee527b84b66e271ae2ba86730	f733d993459329ed1ef9f26d303ba0d9	efc1396e845f3db1688dc8ef154d9520	incollection	Metadata Mechanisms: From Ontology to Folksonomy ... and Back	Stijn Christiaens	\N	2006
7028464	\N	\N	\N	\N	\N	\N	\N	\N	Carnegie Mellon University	\N	\N	\N	Carnegie Mellon University CMU-ISRI-04-105	\N	Tsvetovat2004DyNetML:-Interc	\N	\N		\N	\N	\N	\N	CMU-ISRI-04-105	\N	date-added = {2006-01-10 14:26:36 -0500}, date-modified = {2006-01-10 14:28:13 -0500}	\N	2001d15e92f240cbccf20a5980557e63	6e71b40faa9d15ae4e25edb7dafb0375	e5579e07a85d9102213e188658ff5647	techreport	DyNetML: Interchange Format for Rich Social Network Data	Maksim Tsvetovat and Jeff Reminga and Kathleen M. Carley	\N	2004
7029227	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Harvard Business School Press	Boston	\N	\N	Brown:Life	http://books.google.com/books?id=D-WjL_HRbNQC	\N		\N	\N	\N	\N	\N	\N		\N	b48cd3519b89b50ae759c7e744ff49ae	9a3e9ac501542b592c8e7802f6917601	f31f34c0987c775b49e42cb986c6a3f3	book	The social life of information	John Seely Brown and Paul Duguid	\N	2002
7034082	\N	\N	18	\N	\N	\N	Social Semantic Web	\N	\N	\N	Springer	Berlin, Heidelberg	\N	X.media.press	hotho2008social	http://dx.doi.org/10.1007/978-3-540-72216-8_18	\N	SpringerLink - Buchkapitel	\N	\N	363--391	\N	\N	\N	issn = {1439-3107}, isbn = {978-3-540-72215-1}, vgwort = {49}, doi = {10.1007/978-3-540-72216-8}	BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung
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der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.	16b8f07a41097cc4e4fc962eaf809465	79dbca4289cfe913aa7f7eb7e0dccea7	5ccf05a86e7f1a089ae83dd47568e6de	incollection	Social Bookmarking am Beispiel BibSonomy	Andreas Hotho and Robert Jäschke and Dominik Benz and Miranda Grahl and Beate Krause and Christoph Schmitz and Gerd Stumme	Andreas Blumauer and Tassilo Pellegrini	2009
7034180	Web Semantics: Science, Services and Agents on the World Wide Web	6	\N	\N	Feb	\N	Semantic Web and Web 2.0	\N	\N	\N	Elsevier	New York	\N	\N	jaeschke2008discovering	http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b	\N	ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomies	\N	\N	38--53	\N	1	\N	issn = {1570-8268}, vgwort = {59}, doi = {10.1016/j.websem.2007.11.004}	Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.	3b7846e6fe5d34080ef2397d8764dd36	cfca594f9dbe30694bfbcdeb40dc4e88	18e8babe208fae2c0342438617b0ec31	article	Discovering Shared Conceptualizations in Folksonomies	Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme	T. Finin and R. Mizoguchi and S. Staab	2008
7034196	AI Communications	21	\N	\N	\N	\N	\N	\N	\N	\N	IOS Press	Amsterdam	\N	\N	jaeschke2008tag	http://dx.doi.org/10.3233/AIC-2008-0438	\N		\N	\N	231-247	\N	4	\N	issn = {0921-7126}, vgwort = {63}, doi = {10.3233/AIC-2008-0438}	Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
\
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
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user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences.  We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
\
	9e9f1e49306d787521a1ca4c336ef787	b2f1aba6829affc85d852ea93a8e39f7	955bcf14f3272ba6eaf3dadbef6c0b10	article	Tag Recommendations in Social Bookmarking Systems	Robert Jäschke and Leandro Marinho and Andreas Hotho and Lars Schmidt-Thieme and Gerd Stumme	Enrico Giunchiglia	2008
7034307	\N	\N	\N	\N	January	\N	\N	Hardcover	\N	\N	Addison-Wesley	Boston, MA	\N	\N	citeulike:115158	http://www.amazon.co.uk/exec/obidos/ASIN/0201633612/citeulike-21	\N		\N	\N	\N	\N	\N	\N	citeulike-article-id = {115158}, priority = {0}, isbn = {0201633612}, comment = {hillside link provides the source code.
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macromedia link provides ch. 1 in pdf.
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"One thing expert designers know not to do is solve every problem from first principles. Rather, they reuse solutions that have worked for them in the past. When they find a good solution, they use it again and again. Such experience is part of what makes them experts. Consequently, you’ll find recurring patterns of classes and communicating objects in many object-oriented systems. These patterns solve specific design problems and make object-oriented designs more flexible, elegant, and ultimately reusable. They help designers reuse successful designs by basing new designs on prior experience. A designer who is familiar with such patterns can apply them immediately to design problems without having to rediscover them." (p 1 of intorduction)}	{<I>Design Patterns</I> is a modern classic in the literature of object-oriented development, offering timeless and elegant solutions to common problems in software design. It describes patterns for managing object creation, composing objects into larger structures, and coordinating control flow between objects. The book provides numerous examples where using composition rather than inheritance can improve the reusability and flexibility of code. Note, though, that it's not a tutorial but a catalog that you can use to find an object-oriented design pattern that's appropriate for the needs of your particular application--a selection for virtuoso programmers who appreciate (or require) consistent, well-engineered object-oriented designs.} {Now on CD, this internationally acclaimed bestseller is more valuable than ever! <P> Use the contents of the CD to create your own design documents and reusable components. The CD contains: 23 patterns you can cut and paste into your own design documents; sample code demonstrating pattern implementation; complete Design Patterns content in standard HTML format, with numerous hyperlinked cross-references; accessed through a standard web browser; Java-based dynamic search mechanism, enhancing online seach capabilities; graphical user environment, allowing ease of navigation. <P> First published in 1995, this landmark work on object-oriented software design presents a catalog of simple and succinct solutions to common design problems. Created by four experienced designers, the 23 patterns contained herein have become an essential resource for anyone developing reusable object-oriented software. In response to reader demand, the complete text and pattern catalog are now available on CD-ROM. This electronic version of <i>Design Patterns</i> enables programmers to install the book directly onto a computer or network for use as an online reference for creating reusable object-oriented software. <P> The authors first describe what patterns are and how they can help you in the design process. They then systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. All patterns are compiled from real-world examples and include code that demonstrates how they may be implemented in object-oriented programming languages such as C++ and Smalltalk. Readers who already own the book will want the CD to take advantage of its dynamic search mechanism and ready-to-install patterns.}	bcd93c3d44790cf481626d793daef608	d46ec5e2c98583730aa182ceb4a3ab22	b074c2848d5c64657278632da5ecbd08	book	Design Patterns	Erich Gamma and Richard Helm and Ralph Johnson and John Vlissides	\N	1995
7035692	\N	\N	\N	\N	September	\N	\N	Paperback	\N	\N	Springer	\N	\N	\N	ester2000	http://www.amazon.fr/exec/obidos/ASIN/3540673288/citeulike04-21	\N		\N	\N	\N	\N	\N	\N	isbn = {3540673288}	\N	68cd9d7832899a021a529f7fbdf99c35	ce70e6261519f27b6a1b4e627991f713	598d5d86ef03c20bbded8410f9558eed	book	Knowledge Discovery in Databases : Techniken und Anwendungen	Martin Ester and Jörg Sander	\N	2000
7040471	\N	\N	\N	\N	September	\N	LWA 2007: Lernen - Wissen - Adaption Workshop Proceedings	\N	\N	\N	Martin-Luther-University Halle-Wittenberg	Halle/Saale, Germany	\N	\N	benz07ontology	http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07	\N		\N	\N	109-112	\N	\N	conf/lwa/2007	date = {2007-11-16}, isbn = {978-3-86010-907-6}, vgwort = {16}	\N	01aa067c76952a43107f6add3babcdb2	ff7de5717f771dabd764675279ff3adf	8d29e2f0e3db4dfa1e0f2bd5629d57d5	inproceedings	Position Paper: Ontology Learning from Folksonomies.	Dominik Benz and Andreas Hotho	Alexander Hinneburg	2007
7044654	\N	\N	\N	\N	August	\N	WebKDD 2008 Workshop on Web Mining and Web Usage Analysis	\N	\N	\N	\N	\N	\N	\N	Detecting_Commmunities_via_Simultaneous_Clustering_of_Graphs_and_Folksonomies	\N	\N		\N	To Appear	\N	\N	\N	\N		\N	bf02472ea5d7e6faaae365bb8a88aa30	acfec953843b168e61e2e167e29b4c3d	645abd6b3191a2a6e844d7542651ed1c	inproceedings	Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies	Akshay Java and Anupam Joshi and Tim Finin	\N	2008
7044907	Physical Review E	74	\N	\N	\N	\N	\N	\N	\N	\N	APS	\N	\N	\N	newman2006fcs	\N	\N		\N	\N	36104	\N	3	\N		\N	48f486b769390047d89fd8de70c0f967	5003bcb34d28e1e4bc301fafb9a12c72	090a24e34da3d0ab3d14d61dd3ad3285	article	{Finding community structure in networks using the eigenvectors of matrices}	MEJ Newman	\N	2006
7045427	\N	\N	\N	\N	sep	\N	Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)	\N	\N	\N	Martin-Luther-Universität Halle-Wittenberg	\N	\N	\N	grahl07conceptualKdml	http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf 	\N		\N	\N	50-54	\N	\N	\N	isbn = {978-3-86010-907-6}, vgwort = {14}	\N	73709a1929b98e9d33558558e6077c0c	9c3bb05456bf11bcd88a1135de51f7d9	6d5188d66564fe4ed7386e28868504de	inproceedings	Conceptual Clustering of Social Bookmark Sites	Miranda Grahl and Andreas Hotho and Gerd Stumme	Alexander Hinneburg	2007
7046267	\N	\N	\N	\N	May	\N	Proceedings of the WWW 2006 Workshop on Collaborative Web Tagging \	Workshop	\N	\N	\N	\N	Edinburgh	\N	\N	Begelman2006	http://www.rawsugar.com/www2006/taggingworkshopschedule.html	\N		\N	\N	\N	\N	\N	\N	timestamp = {2007.04.11}, pdf = {http://www.rawsugar.com/www2006/20.pdf}	\N	882669a1264ab98f6e6ca9ff98d2bf94	ffacd9d40f6cba1aa8140f501c2a1802	95449b3d4b12e8930d529e1e22d51e04	inproceedings	Automated Tag Clustering: Improving search and exploration in the tag space	Grigory Begelman and Philipp Keller and Frank Smadja	\N	2006
7046293	Web Semantics: Science, Services and Agents on the World Wide Web	5	\N	\N	March	\N	Selected Papers from the International Semantic Web Conference, International Semantic Web Conference (ISWC2005)	\N	\N	\N	\N	\N	\N	\N	citeulike:1220636	http://dx.doi.org/10.1016/j.websem.2006.11.002	\N		\N	\N	5--15	\N	1	\N	posted-at = {2008-11-18 14:30:12}, citeulike-article-id = {1220636}, priority = {5}, doi = {http://dx.doi.org/10.1016/j.websem.2006.11.002}	In our work the traditional bipartite model of ontologies is extended with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.	461eb5a3a69bbfb433207eb5cabd70d5	5bba04607af19c94d2438ae13f362649	13eb27ecb7ed77655f08adefe6186ea5	article	Ontologies are us: A unified model of social networks and semantics	Peter Mika	\N	2007
7046301	\N	\N	\N	\N	May	\N	Proceedings of the Workshop on Collaborative Tagging at WWW2006	\N	\N	\N	\N	Edinburgh, Scotland	\N	\N	schmitz06-inducing	http://.citeulike.org/user/ryanshaw/article/740688	\N		\N	\N	\N	\N	\N	\N	pdf = {schmitz06-inducing.pdf}, lastname = {Schmitz}, lastdatemodified = {2006-10-12}, read = {readnext}, own = {own}	In this paper, we describe some promising initial results in inducing ontology from the Flickr tag vocabulary, using a subsumption-based model. We describe the utility of faceted ontology as a supplement to a tagging system and present our model and results. We propose a revised, probabilistic model using seed ontologies to induce faceted ontology, and describe how the model can integrate into the logistics of tagging communities.	e2120caa42918f199240b8b7ee275720	1335f4ef87f951e6edf4fd94f885d3a2	f913a4ad3a27582ae5d4d269fe38dc5c	inproceedings	Inducing Ontology from Flickr Tags	Patrick Schmitz	\N	2006
7046404	\N	\N	\N	\N	\N	\N	Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability - Vol. 1	\N	\N	\N	University of California Press, Berkeley, CA, USA	\N	\N	\N	mcqueen1967smc	http://projecteuclid.org/euclid.bsmsp/1200512992	\N		\N	\N	281--297	\N	\N	\N		\N	decb3581278889d4e7c4a37d3bda88d0	8d7d4dfe7d3a06b8c9c3c2bb7aa91e28	d23dfdff44ca5121fde221604128ab80	inproceedings	Some Methods for Classification and Analysis of Multivariate Observations	J. MacQueen	L. M. {Le Cam} and J. Neyman	1967
7046529	\N	\N	\N	\N	\N	\N	WWW '08: Proceeding of the 17th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Singla08	http://portal.acm.org/citation.cfm?doid=1367497.1367586	\N	Yes, there is a correlation	\N	\N	655--664	\N	\N	\N	location = {Beijing, China}, isbn = {978-1-60558-085-2}, doi = {http://doi.acm.org/10.1145/1367497.1367586}	\N	3bae26e5a074031eb4c9420932181d8d	6dbd0dcbb9c4e19b737d295d87d707be	a5bbe128c30c50e5830fd23c05f0389a	inproceedings	Yes, there is a correlation: - from social networks to personal behavior on the web	Parag Singla and Matthew Richardson	\N	2008
7046658	AI Communications	20	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	cattuto2007	http://www.kde.cs.uni-kassel.de/hotho/pub/2007/aicomm_2007_folksonomy_clustering.pdf	\N		\N	\N	245 - 262	\N	4	\N	vgwort = {67}	\N	d70721ce8ec7ffd8dcc8975c0a2c9fc8	fc5f2df61d28bc99b7e15029da125588	d87e198a6d564ae8a8fe151e0a96fa0f	article	Network Properties of Folksonomies	C. Cattuto and C. Schmitz and A. Baldassarri and V. D. P. Servedio and V. Loreto and A. Hotho and M. Grahl and G. Stumme	\N	2007
7048500	kommunikation@gesellschaft	Jg. 8	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	hammwoehner_qualitaetsaspekte-der-wikipedia_2007	http://www.soz.uni-frankfurt.de/K.G/B3_2007_Hammwoehner.pdf	\N		\N	\N	\N	\N	3	\N		\N	86f54be3b41c93fc890382befb043fb6	8d55b70a8f3a52dccfd6e8889aab82e7	a430801767ba3ddffcaa42af227be9e6	article	Qualitätsaspekte der Wikipedia	Rainer Hammwöhner	\N	2007
7053091	eLearn	2005	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Downes2005	http://dx.doi.org/http://dx.doi.org/http://portal.acm.org/citation.cfm?doid=1104966.1104968	\N		\N	\N	1	\N	10	\N	posted-at = {2009-01-04 19:07:19}, citeulike-article-id = {3847996}, priority = {2}, doi = {http://dx.doi.org/http://dx.doi.org/http://portal.acm.org/citation.cfm?doid=1104966.1104968}	\N	ac83f5e0109c34f131c287a4c84779ed	d248ef5cb4cb4f7285fd30a42b624651	9fa4eb44b17f332f437b1dd447a8aa5d	article	E-learning 2.0	Stephen Downes	\N	2005
7053096	Techreport	\N	\N	\N	Feb	\N	\N	\N	\N	\N	\N	\N	\N	\N	Anderson2007	http://www.jisc.ac.uk/media/documents/techwatch/tsw0701b.pdf	\N		\N	\N	\N	\N	\N	\N	posted-at = {2009-01-04 19:07:19}, priority = {2}, citeulike-article-id = {3847991}	\N	ffb553320c6ab565269cfc8b5387506d	0b46651a30e080406de71b3c91450f93	10e5fecca8a589849a52108c8c051f32	techreport	What is Web 2.0? Ideas, technologies and implications for education	P. Anderson	\N	2007
7053121	PhD Thesis	\N	\N	\N	Mar	\N	\N	\N	\N	\N	\N	\N	\N	\N	Dolog2006	http://www.cs.aau.dk/\\%5C\\~{}dolog/pub/dolog\\%5C\\_phd\\%5C\\_thesis.pdf	\N		\N	\N	\N	\N	\N	\N	posted-at = {2009-01-04 19:07:18}, priority = {2}, citeulike-article-id = {3847966}	\N	902c63429fcf12e82e30ab8844749ed0	168f24cd1624d2aacf0e5b9e1dcbc0a5	2c387c3b57d71551c289f728a603b82d	phdthesis	Engineering Adaptive Web Applications	Peter Dolog	\N	2006
7053143	Proceedings	\N	\N	\N	Sep	\N	\N	\N	\N	\N	\N	\N	\N	\N	wolperspromoting04	http://www.kbs.uni-hannover.de/Arbeiten/Publikationen/2004/icl04.pdf	\N		\N	\N	\N	\N	\N	\N	posted-at = {2009-01-04 19:07:16}, priority = {2}, citeulike-article-id = {3847941}	Despite the large number of efforts European research still is quite heterogeneous in the area of technology enhanced professional learning. The Network of Excellence in professional learning (PROLEARN) aims at bridging the existing gap between academic efforts and industrial needs to facilitate research results in industrial eLearning applications. Its research activities in seven identified key areas are complemented by the PROLEARN virtual competence center used to spread excellence through European companies, thus acting as intermediary between research and economy. Focusing on the educational aspects and providing high-level education, the PROLEARN Academy coordinates the research activities, guided by an ongoing roadmapping effort and resulting in a new understanding of co-operation among the various involved parties.	7da3aed0ca44c5e1105174421d2ca5d3	9c0152e080018d425a84fd15579b5a04	4830800fd4268e2aa90c52e4a26bc3aa	inproceedings	Promoting E-Learning Research and Application Scenarios in Europe	Martin Wolpers	\N	2004
7053146	Proceedings	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Ullrich2008	http://dx.doi.org/http://dx.doi.org/http://doi.acm.org/10.1145/1367497.1367593	\N		\N	\N	705--714	\N	\N	\N	posted-at = {2009-01-04 19:07:16}, priority = {2}, citeulike-article-id = {3847938}, doi = {http://dx.doi.org/http://dx.doi.org/http://doi.acm.org/10.1145/1367497.1367593}	\N	20f0744614b3f0a5904344d680b1447c	259f62de8041c77f1e7ee02e02767e6b	978b40ff0a4b622ea34088d7448e7f8d	inproceedings	Why web 2.0 is good for learning and for research: principles and prototypes	Carsten Ullrich and Kerstin Borau and Heng Luo and Xiaohong Tan and Liping Shen and Ruimin Shen	\N	2008
7054106	\N	\N	14	\N	\N	\N	The SMART Retrieval System: Experiments in Automatic Document Processing	\N	\N	\N	Prentice-Hall, Englewood Cliffs NJ	\N	\N	Prentice-Hall Series in Automatic Computation	Rocchio:71	\N	\N	PhD	\N	\N	313--323	\N	\N	\N		\N	94a7182b00bb2ebb11824b912290acbd	c18d843e34fe4f8bd1d2438227857225	b529e8965fb8ef1524dacbb81d016b9b	incollection	Relevance feedback in information retrieval	J. J. Rocchio	G. Salton	1971
7054114	\N	\N	\N	\N	August February--May	\N	17th Conference on Uncertainty in Artificial Intelligence	\N	\N	\N	\N	Seattle, Washington	\N	\N	Popescul:UAI2001	http://citeseer.ist.psu.edu/popescul01probabilistic.html	\N	PhD	\N	\N	437--444	\N	\N	\N	priority = {3}	Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not...	15a4633831beaa4b6bacad0fc1b95bcd	429bcf0381d2b7b9ab95eea7d3a65776	ae7ce7b8d1a31e81f9aa8b8367039506	inproceedings	Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments	Alexandrin Popescul and Lyle Ungar and David Pennock and Steve Lawrence	\N	2001
7054130	\N	\N	\N	\N	\N	\N	Proceedings of the 11th international conference on Intelligent User Interfaces	\N	\N	\N	ACM	New York, NY, USA	\N	\N	McCarthy:IUI06	\N	\N	PhD	\N	\N	267--269	\N	\N	\N	location = {Sydney, Australia}, isbn = {1-59593-287-9}, doi = {http://doi.acm.org/10.1145/1111449.1111506}	\N	b735e0ba8af522fcca3940c1104b3240	0cf550dbe83d9c45cd49a02fa209cea8	c744beb9a9389fa2544f4e64c69f116d	inproceedings	Group recommender systems: a critiquing based approach	Kevin McCarthy and Maria Salam\\'{o} and Lorcan Coyle and Lorraine McGinty and Barry Smyth and Paddy Nixon	\N	2006
7054134	Frontiers of WWW Research and Development	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Yanfei:WWWResearch06	http://dx.doi.org/10.1007/11610113_66	\N	SpringerLink	\N	\N	733--738	\N	\N	\N		Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.\
ER  -	45d1e09f181f6972871b9afce258ce35	edf999afa5a0ff81e53b0c859b466659	64d7db1df158a73984b5483873c42cfd	article	Cubic Analysis of Social Bookmarking for Personalized Recommendation	Yanfei Xu and Liang Zhang and Wei Liu	\N	2006
7054139	Communications of the ACM	43	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Mobasher:ACM2000	\N	\N	PhD	\N	\N	142--151	\N	8	\N	issn = {0001-0782}, doi = {http://doi.acm.org/10.1145/345124.345169}	\N	d8cd8a4d7502a49c7fe70c2bbafcdc23	98d5090dafb39596483c75dc4a6846c3	6bc183a1daccbb823f7fa656f80917e7	article	Automatic personalization based on Web usage mining	Bamshad Mobasher and Robert Cooley and Jaideep Srivastava	\N	2000
7054144	SIGKDD Explorations	2	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Kosala00webmining	\N	\N	PhD	\N	\N	1--15	\N	\N	\N		\N	7cf09191ccd444185a2377536985f0e6	99eea914954da48c9691277ce4e32932	0afb1f9a700105d920699ab6a1a5b1b3	article	Web mining research: A survey	Raymond Kosala and Hendrik Blockeel	\N	2000
7054147	\N	\N	\N	\N	\N	\N	In Proceedings of iTrust2004 International Conference	\N	\N	\N	\N	\N	\N	\N	Massa04usingtrust	\N	\N	PhD	\N	\N	221--235	\N	\N	\N		Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use it as a weight for the users’ ratings. However they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. We assert that these weaknesses can be alleviated using a Trust-aware system that takes into account the “web of trust ” provided by every user. Specifically, we analyze data from the popular Internet web site epinions.com. The dataset consists of 49290 users who expressed reviews (with rating) on items and explicitly specified their web of trust, i.e. users whose reviews they have consistently found to be valuable. We show that any two users have usually few items rated in common. For this reason, the classic RS technique is often ineffective and is not able to compute a user similarity weight for many of the users. Instead exploiting the webs of trust, it is possible to propagate trust and infer an additional weight for other users. We show how this quantity can be computed against a larger number of users.	f59991217edbe456c2620ec026fd5320	7c86a954ff4d47c87c48a14b2128188b	f58cdc513789be4851b1f5fc38043b3b	inproceedings	Using trust in recommender systems: an experimental analysis	Paolo Massa and Bobby Bhattacharjee	\N	2004
7054148	\N	\N	\N	\N	\N	\N	Proceedings of the 10th international conference on Intelligent user interfaces	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Donovan:IUI05	\N	\N	PhD	\N	\N	167--174	\N	\N	\N	location = {San Diego, California, USA}, isbn = {1-58113-894-6}, doi = {http://doi.acm.org/10.1145/1040830.1040870}	Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.	0a8b3c6a63a98ce73c7b63ea1c5a2133	6b3655ff8906dd91f5e34c0a6848c81e	ee75f068830852c96b2cf766044174b8	inproceedings	Trust in recommender systems	John O'Donovan and Barry Smyth	\N	2005
7054149	\N	\N	\N	\N	\N	\N	Proceedings of the 2000 ACM conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Herlocker:ACM2000	\N	\N	PhD	\N	\N	241--250	\N	\N	\N	location = {Philadelphia, Pennsylvania, United States}, isbn = {1-58113-222-0}, doi = {http://doi.acm.org/10.1145/358916.358995}	\N	1155789b34ce32f2d822d2ea369f2fb2	92273b87585b39bd394cb77f5a81ff1f	6a365614a619cf27c30c72fea870a6a9	inproceedings	Explaining collaborative filtering recommendations	Jonathan L. Herlocker and Joseph A. Konstan and John Riedl	\N	2000
7054160	Science	311	\N	\N	February	\N	\N	\N	\N	\N	\N	Department of Sociology, 413 Fayerweather Hall, Columbia University, New York, NY, 10027, USA. mjs2105@columbia.edu	\N	\N	Salganik:Science2006	http://dx.doi.org/10.1126/science.1121066	\N	PhD	\N	\N	854--856	\N	5762	\N	issn = {1095-9203}, citeulike-article-id = {520717}, priority = {0}, doi = {10.1126/science.1121066}	Hit songs, books, and movies are many times more successful than average, suggesting that the best alternatives are qualitatively different from the rest; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial music market in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.	de2ce60baca69247a2d46a8bbe249a93	ed0602aedd59f119bafa02e8fdfba4be	d0012c6e17e067452904edf458f84dca	article	Experimental study of inequality and unpredictability in an artificial cultural market.	M. J. Salganik and P. S. Dodds and D. J. Watts	\N	2006
7054166	\N	\N	\N	\N	\N	\N	Proceedings of SIGCHI conference on Human factors in computing systems	\N	\N	\N	ACM Press/Addison-Wesley Publishing Co.	New York, USA	\N	\N	Maltz:CHI95	\N	\N	PhD	\N	\N	202-209	\N	\N	\N	location = {Denver, Colorado, United States}, isbn = {0-201-84705-1}, doi = {http://doi.acm.org/10.1145/223904.223930}	\N	1f29c0e21f59124bbb23e511ec10fe25	4bf91061691bf416b6db63c6ac781e36	97027c48bacfe1d89039e40ebe5180ce	inproceedings	Pointing the way: active collaborative filtering	 D.Maltz and K. Ehrlich	\N	1995
7054168	\N	\N	\N	\N	\N	\N	Proceedings of the 2007 Latin American Web Conference (LA-WEB)	\N	\N	\N	IEEE Computer Society	Washington, DC, USA	\N	\N	Firan:LAWEB07	\N	\N	PhD	\N	\N	32-41	\N	\N	\N	isbn = {0-7695-3008-7}, doi = {http://dx.doi.org/10.1109/LA-WEB.2007.24}	\N	f67ce0d10933076767e683433b26426c	3261a775322d01ef56865452946a33f6	cd4de3ac1238d77b00ca7d7920b06a8c	inproceedings	The Benefit of Using Tag-Based Profiles	Claudiu S. Firan and Wolfgang Nejdl and Raluca Paiu	\N	2007
7054170	\N	\N	\N	\N	\N	\N	Proceedings of SIGCHI conference on Human factors in computing systems	\N	\N	\N	\N	New York, USA	\N	\N	Hill:CHI95	\N	\N	PhD	\N	\N	194-201	\N	\N	\N	location = {Denver, Colorado, United States}, isbn = {0-201-84705-1}, doi = {http://doi.acm.org/10.1145/223904.223929}	\N	29c5a80283c7e8f1147c633465179694	e3a0e169967604162a0d34731ebbdef2	63d720db259a47724a24820071402b2f	inproceedings	Recommending and evaluating choices in a virtual community of use	W. Hill and L. Stead and M. Rosenstein and G. Furnas	\N	1995
7054180	Communications of the ACM	40	\N	\N	\N	\N	\N	\N	\N	\N	ACM Press	New York, NY, USA	\N	\N	Resnick:ACM97	\N	\N	PhD	\N	\N	56-58	\N	3	\N	issn = {0001-0782}, doi = {http://doi.acm.org/10.1145/245108.245121}	\N	4835e3debbf2e14bc915fb862fcbc4e9	cf0a6284576ebbb131680252a607d75a	6996e6e13254f4c542e72890b884fd85	article	Recommender systems	P. Resnick and H. R. Varian	\N	1997
7058870	Web Semantics: Science, Services and Agents on the World Wide Web	6	\N	\N	Feb	\N	Semantic Web and Web 2.0	\N	\N	\N	Elsevier	New York	\N	\N	jaeschke2008discovering	http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b	\N	ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomies	\N	\N	38--53	\N	1	\N	issn = {1570-8268}, vgwort = {59}, doi = {10.1016/j.websem.2007.11.004}	Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.	3b7846e6fe5d34080ef2397d8764dd36	cfca594f9dbe30694bfbcdeb40dc4e88	18e8babe208fae2c0342438617b0ec31	article	Discovering Shared Conceptualizations in Folksonomies	Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme	T. Finin and R. Mizoguchi and S. Staab	2008
7076902	\N	\N	\N	\N	May	\N	Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland	\N	\N	\N	\N	\N	\N	\N	schmitz2006flickrTags	http://www.rawsugar.com/www2006/22.pdf	\N		\N	\N	\N	\N	\N	\N	local-url = {file://localhost/Users/knud/Documents/DERI/Papers/bucket/Inducing%20Ontology%20from%20Flickr%20Tags.pdf}, priority = {2}, citeulike-article-id = {699178}	\N	bd41aca2d727954d05094e933fb162f7	1335f4ef87f951e6edf4fd94f885d3a2	00001781e46f97dffc01bf80b224e475	inproceedings	{Inducing Ontology from Flickr Tags}	Patrick Schmitz	\N	2006
7077135	The Semantic Web – ISWC 2005	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	mika2005ontology	http://dx.doi.org/10.1007/11574620_38	\N		\N	\N	522--536	\N	\N	\N		In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.
\
ER  -	64944a9d7d2f9d53396104020d2c2216	5ea12110b5bb0e3a8ad09aeb16a70cdb	12bba91c38e6cdbec4e8a9372d1f3891	article	Ontologies Are Us: A Unified Model of Social Networks and Semantics	Peter Mika	\N	2005
7077214	\N	5318	\N	\N	\N	\N	The Semantic Web - ISWC 2008, Proc.Intl. Semantic Web Conference 2008	\N	\N	\N	Springer	Heidelberg	\N	LNAI	cattuto2008semantic	http://dx.doi.org/10.1007/978-3-540-88564-1_39	\N		\N	\N	615--631	\N	\N	\N		Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks
\
like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.	34a956f5950fa759cb21bde88e59d7bb	b44538648cfd476d6c94e30bc6626c86	cb5a778df139d78c2b6e6533155dabd8	inproceedings	Semantic Grounding of Tag Relatedness in Social Bookmarking Systems	Ciro Cattuto and Dominik Benz and Andreas Hotho and Gerd Stumme	Amith Sheth et al.	2008
7090055	\N	\N	\N	\N	August	\N	Proceedings of the Tenth Workshop on Web Mining and Web Usage Analysis (WebKDD)	\N	\N	\N	ACM	\N	\N	\N	Detecting_Commmunities_via_Simultaneous_Clustering_of_Graphs_and_Folksonomies	http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies	\N		\N	Held in conjunction with The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008)	\N	\N	\N	\N		We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information that is readily available in most social media systems. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently to within this set than outside it and they share similar tags. Our technique is based on the Normalized Cut (NCut) algorithm and can be easily and efficiently implemented. We validate our method by using a real network of blogs and tag information obtained from a social bookmarking site. We also verify our results on a citation network for which we have access to ground truth cluster information. Our method, Simultaneous Cut (SimCut), has the advantage that it can group related tags and cluster the nodes simultaneously.	074c14fe5113292ba62714a15cf47651	acfec953843b168e61e2e167e29b4c3d	80e4f692625afcf21e8195915ea93de0	inproceedings	Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies	Akshay Java and Anupam Joshi and Tim Finin	\N	2008
7090768	\N	4956	\N	\N	April	\N	30th European Conference on IR Research, ECIR 2008	\N	\N	\N	Springer	Glasgow, UK	\N	Lecture Notes in Computer Science	krause08social	\N	\N		\N	\N	101-113	\N	\N	\N	misc = {vgwort=21,1}, isbn = {978-3-540-78645-0}	\N	7ee418bbed426a12581861fde4d6c360	37598733b747093d97a0840a11beebf5	ad5f4664d1f5b967a0793b4a26a0edbb	inproceedings	A Comparison of Social Bookmarking with Traditional Search	Beate Krause and Andreas Hotho and Gerd Stumme	Craig Macdonald and Iadh Ounis and Vassilis Plachouras and Ian Ruthven and Ryen W. White	2008
7090784	\N	\N	\N	\N	\N	\N	AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	krause2008anti	\N	\N		\N	\N	61--68	\N	\N	\N	vg-wort = {29.6}, location = {Beijing, China}, isbn = {978-1-60558-159-0}, doi = {http://doi.acm.org/10.1145/1451983.1451998}	\N	74cf98f26a6b5b2604cc3592fb5c757f	a45d40ac7776551301ad9dde5b25357f	93af1487543e0395ee8f7e0413bbcfc1	inproceedings	The anti-social tagger: detecting spam in social bookmarking systems	Beate Krause and Christoph Schmitz and Andreas Hotho and Gerd Stumme	\N	2008
7090806	\N	\N	\N	\N	\N	\N	Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)	\N	\N	\N	AAAI Press	\N	\N	\N	jaeschke2008logsonomy	http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf	\N		\N	\N	\N	\N	\N	\N	vgwort = {7}	In social bookmarking systems users describe bookmarks
\
by keywords called tags. The structure behind
\
these social systems, called folksonomies, can be
\
viewed as a tripartite hypergraph of user, tag and resource
\
nodes. This underlying network shows specific
\
structural properties that explain its growth and the possibility
\
of serendipitous exploration.
\
Search engines filter the vast information of the web.
\
Queries describe a user’s information need. In response
\
to the displayed results of the search engine, users click
\
on the links of the result page as they expect the answer
\
to be of relevance. The clickdata can be represented as a
\
folksonomy in which queries are descriptions of clicked
\
URLs. This poster analyzes the topological characteristics
\
of the resulting tripartite hypergraph of queries,
\
users and bookmarks of two query logs and compares it
\
two a snapshot of the folksonomy del.icio.us.	507a542f53184fd2838208d7bee9343b	13ec3f45fc7e0364cdc6b9a7c12c5c2c	359e1eccdc524334d4a2ad51330f76ae	inproceedings	Logsonomy — A Search Engine Folksonomy	Robert Jäschke and Beate Krause and Andreas Hotho and Gerd Stumme	\N	2008
7090815	\N	\N	\N	\N	\N	\N	HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia	\N	\N	\N	ACM	New York, NY, USA	\N	\N	krause2008logsonomy	http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia	\N		\N	\N	157--166	\N	\N	\N	location = {Pittsburgh, PA, USA}, isbn = {978-1-59593-985-2}, vgwort = {17}, doi = {http://doi.acm.org/10.1145/1379092.1379123}	Social bookmarking systems constitute an established
\
part of the Web 2.0. In such systems
\
users describe bookmarks by keywords
\
called tags. The structure behind these social
\
systems, called folksonomies, can be viewed
\
as a tripartite hypergraph of user, tag and resource
\
nodes. This underlying network shows
\
specific structural properties that explain its
\
growth and the possibility of serendipitous
\
exploration.
\
Today’s search engines represent the gateway
\
to retrieve information from the World Wide
\
Web. Short queries typically consisting of
\
two to three words describe a user’s information
\
need. In response to the displayed
\
results of the search engine, users click on
\
the links of the result page as they expect
\
the answer to be of relevance.
\
This clickdata can be represented as a folksonomy
\
in which queries are descriptions of
\
clicked URLs. The resulting network structure,
\
which we will term logsonomy is very
\
similar to the one of folksonomies. In order
\
to find out about its properties, we analyze
\
the topological characteristics of the tripartite
\
hypergraph of queries, users and bookmarks
\
on a large snapshot of del.icio.us and
\
on query logs of two large search engines.
\
All of the three datasets show small world
\
properties. The tagging behavior of users,
\
which is explained by preferential attachment
\
of the tags in social bookmark systems, is
\
reflected in the distribution of single query
\
words in search engines. We can conclude
\
that the clicking behaviour of search engine
\
users based on the displayed search results
\
and the tagging behaviour of social bookmarking
\
users is driven by similar dynamics.	8cad4b9ac159b185b03f39b60d8691d7	6d34ea1823d95b9dbf37d4db4d125d2a	76d81124951ae39060a8bc98f4883435	inproceedings	Logsonomy - Social Information Retrieval with Logdata	Beate Krause and Robert Jäschke and Andreas Hotho and Gerd Stumme	\N	2008
7098027	AI Communications	21	\N	\N	\N	\N	\N	\N	\N	\N	IOS Press	Amsterdam	\N	\N	jaeschke2008tag	http://dx.doi.org/10.3233/AIC-2008-0438	\N		\N	\N	231-247	\N	4	\N	issn = {0921-7126}, vgwort = {63}, doi = {10.3233/AIC-2008-0438}	Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
\
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
\
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences.  We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
\
	9e9f1e49306d787521a1ca4c336ef787	b2f1aba6829affc85d852ea93a8e39f7	955bcf14f3272ba6eaf3dadbef6c0b10	article	Tag Recommendations in Social Bookmarking Systems	Robert Jäschke and Leandro Marinho and Andreas Hotho and Lars Schmidt-Thieme and Gerd Stumme	Enrico Giunchiglia	2008
7098029	\N	\N	18	\N	\N	\N	Social Semantic Web	\N	\N	\N	Springer	Berlin, Heidelberg	\N	X.media.press	hotho2008social	http://dx.doi.org/10.1007/978-3-540-72216-8_18	\N	SpringerLink - Buchkapitel	\N	\N	363--391	\N	\N	\N	issn = {1439-3107}, isbn = {978-3-540-72215-1}, vgwort = {49}, doi = {10.1007/978-3-540-72216-8}	BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung
\
der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.	16b8f07a41097cc4e4fc962eaf809465	79dbca4289cfe913aa7f7eb7e0dccea7	5ccf05a86e7f1a089ae83dd47568e6de	incollection	Social Bookmarking am Beispiel BibSonomy	Andreas Hotho and Robert Jäschke and Dominik Benz and Miranda Grahl and Beate Krause and Christoph Schmitz and Gerd Stumme	Andreas Blumauer and Tassilo Pellegrini	2009
7103319	\N	\N	\N	\N	\N	\N	Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)	\N	\N	\N	AAAI Press	Menlo Park, CA, USA	\N	\N	Jaeschke2008logsonomy	http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf	\N		\N	\N	\N	\N	\N	\N	isbn = {978-1-57735-355-3}, vgwort = {7}	In social bookmarking systems users describe bookmarks
\
by keywords called tags. The structure behind
\
these social systems, called folksonomies, can be
\
viewed as a tripartite hypergraph of user, tag and resource
\
nodes. This underlying network shows specific
\
structural properties that explain its growth and the possibility
\
of serendipitous exploration.
\
Search engines filter the vast information of the web.
\
Queries describe a user’s information need. In response
\
to the displayed results of the search engine, users click
\
on the links of the result page as they expect the answer
\
to be of relevance. The clickdata can be represented as a
\
folksonomy in which queries are descriptions of clicked
\
URLs. This poster analyzes the topological characteristics
\
of the resulting tripartite hypergraph of queries,
\
users and bookmarks of two query logs and compares it
\
two a snapshot of the folksonomy del.icio.us.	507a542f53184fd2838208d7bee9343b	13ec3f45fc7e0364cdc6b9a7c12c5c2c	359e1eccdc524334d4a2ad51330f76ae	inproceedings	Logsonomy — A Search Engine Folksonomy	Robert Jäschke and Beate Krause and Andreas Hotho and Gerd Stumme	\N	2008
7104369	\N	\N	\N	\N	\N	\N	RE	\N	\N	\N	IEEE Computer Society	\N	\N	\N	Liaskos06	http://dblp.uni-trier.de/db/conf/re/re2006.html#LiaskosLYYM06	\N	dblp	\N	\N	76-85	\N	\N	conf/re/2006	date = {2008-01-22}, ee = {http://doi.ieeecomputersociety.org/10.1109/RE.2006.45}, isbn = {0-7695-2555-5}	\N	a6643d480eebbca6553f3622e0a0d3ea	c1531273cae02f80574cd1e419b60917	2afd38ff274361e680f300ad6ed5d752	inproceedings	On Goal-based Variability Acquisition and Analysis.	Sotirios Liaskos and Alexei Lapouchnian and Yijun Yu and Eric S. K. Yu and John Mylopoulos	\N	2006
7104419	\N	\N	\N	\N	\N	\N	InScit2006: International Conference on Multidisciplinary Information \	Sciences and Technologies	\N	\N	\N	\N	\N	\N	\N	HaHe06	http://nosolousabilidad.com/hassan/improving_tagclouds.pdf	\N		\N	\N	\N	\N	\N	\N	timestamp = {2008.01.14}, file = {HaHe06.pdf:folksonomies\\\\HaHe06.pdf:PDF}, owner = {michael}, misc = {comment = {proposes using k-clustering and some sort of semantic sorting
\
\	to refactor tag cloud layout to improve browsing. Not clear on how
\
\	they actually do it.}, priority = {0}, citeulike-article-id = {2045619}}	Tagging-based systems enable users to categorize web resources by
\
\	means of tags (freely chosen keywords), in order to re-finding these
\
\	resources later. Tagging is implicitly also a social indexing process,
\
\	since users share their tags and resources, constructing a social
\
\	tag index, so-called folksonomy. At the same time of tagging-based
\
\	system, has been popularised an interface model for visual information
\
\	retrieval known as Tag-Cloud. In this model, the most frequently
\
\	used tags are displayed in alphabetical order. This paper presents
\
\	a novel approach to Tag-Cloud�s tags selection, and proposes the
\
\	use of clustering algorithms for visual layout, with the aim of improve
\
\	browsing experience. The results suggest that presented approach
\
\	reduces the semantic density of tag set, and improves the visual
\
\	consistency of Tag-Cloud layout.	5a11abca4ed37caa09635704aac2bfe5	4458142370e3c6a4fe656af2f822a0dc	06f68f9fe46dc6d0f646d932e428dec9	inproceedings	Improving Tag-Clouds as Visual Information Retrieval Interfaces	Y. Hassan-Montero and V. Herrero-Solana	\N	2006
7104464	\N	\N	\N	\N	\N	\N	Proc. of the Fourth International Workshop on  Adversarial Information Retrieval on the Web	\N	\N	\N	\N	\N	\N	\N	krause2008antisocial	http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf	\N		\N	\N	\N	\N	\N	\N		\N	58d6e8ff3f5a3d30d1839a415181db8c	a45d40ac7776551301ad9dde5b25357f	6357f535000a383f228f1e8e56ca86ca	inproceedings	The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems	Beate Krause and Christoph Schmitz and Andreas Hotho and Gerd Stumme	\N	2008
7104474	AI Communications	21	\N	\N	\N	\N	\N	\N	\N	\N	IOS Press	Amsterdam	\N	\N	jaeschke2008tag	http://dx.doi.org/10.3233/AIC-2008-0438	\N		\N	\N	231-247	\N	4	\N	issn = {0921-7126}, vgwort = {63}, doi = {10.3233/AIC-2008-0438}	Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
\
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
\
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences.  We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
\
	9e9f1e49306d787521a1ca4c336ef787	b2f1aba6829affc85d852ea93a8e39f7	955bcf14f3272ba6eaf3dadbef6c0b10	article	Tag Recommendations in Social Bookmarking Systems	Robert Jäschke and Leandro Marinho and Andreas Hotho and Lars Schmidt-Thieme and Gerd Stumme	Enrico Giunchiglia	2008
7104780	AI Communications	21	\N	\N	\N	\N	\N	\N	\N	\N	IOS Press	Amsterdam	\N	\N	jaeschke2008tag	http://dx.doi.org/10.3233/AIC-2008-0438	\N		\N	\N	231-247	\N	4	\N	issn = {0921-7126}, vgwort = {63}, doi = {10.3233/AIC-2008-0438}	Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
\
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
\
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences.  We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
\
	9e9f1e49306d787521a1ca4c336ef787	b2f1aba6829affc85d852ea93a8e39f7	955bcf14f3272ba6eaf3dadbef6c0b10	article	Tag Recommendations in Social Bookmarking Systems	Robert Jäschke and Leandro Marinho and Andreas Hotho and Lars Schmidt-Thieme and Gerd Stumme	Enrico Giunchiglia	2008
7106775	\N	\N	\N	\N	\N	\N	Handbook on Ontologies	\N	\N	\N	Springer	\N	\N	International Handbooks on Information Systems	Staab04	\N	\N	DBLP Record 'books/sp/StaabS04'	\N	\N	\N	\N	\N	\N	bibsource = {DBLP, http://dblp.uni-trier.de}, isbn = {3-540-40834-7}	\N	6378766685a5b8c6c201639d31fd70c0	494a7427b9dd11496d824c824b35938b	28269590c9d9c1660c1d7c98a73a28e1	book	Handbook on Ontologies	\N	Steffen Staab and Rudi Studer	2004
7125018	J. Am. Soc. Inf. Sci. Technol.	52	\N	\N	\N	\N	\N	\N	\N	\N	John Wiley \\& Sons, Inc.	New York, NY, USA	\N	\N	Spink01	http://portal.acm.org/citation.cfm?id=362968.362979	\N	Searching the Web	\N	\N	226--234	\N	3	\N	issn = {1532-2882}, doi = {http://dx.doi.org/10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.3.CO;2-I}	\N	fa0470b942bbc97e32ad2510fc447b71	bd687cbfe802b1e2a8f722de018de5be	32b282c8b48b4dde5d06d01b63dbbe32	article	Searching the Web: the public and their queries	Amanda Spink and Dietmar Wolfram and Major B. J. Jansen and Tefko Saracevic	\N	2001
7128317	\N	123	\N	\N	JUL	\N	\N	\N	\N	\N	IOS Press	\N	\N	Frontiers in Artificial Intelligence	buitelaar05ontologylearningbook	\N	\N		\N	\N	\N	\N	\N	\N		\N	6a2916ece8f555d44715ff32fbcf1cf8	9a5beec1eb7d58ead91f134915be86ab	0e71ddd52894af0e681b9d9411f7944f	book	Ontology Learning from Text: Methods, Evaluation and Applications	\N	Paul Buitelaar and Philipp Cimiano and Bernardo Magnini	2005
7134636	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	MIT	\N	mui:2002	http://groups.csail.mit.edu/medg/ftp/lmui/computational%20models%20of%20trust%20and%20reputation.pdf	\N	PhD thesis on trust and eputation modeling	\N	\N	\N	\N	\N	\N		Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts.
\
We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. “Better” is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and  reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to void. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater.
\
Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to.	db9c00e7d302ccce99a5dea994b1edc9	f13e3c182e381bdae1158c45a944b16b	a77ce2c7736be8db25f61ed332cff521	phdthesis	Computational Models of Trust and Reputation:  Agents, Evolutionary Games, and Social Networks	Lik Mui	\N	2002
7140950	\N	\N	\N	\N	\N	\N	RANLP 2007, CALP workshop	\N	\N	\N	\N	Borovets, Bulgaria	\N	\N	Picca2007	http://moromete.net/articles/picca_et_al_calp07_cr.pdf	\N	suggested by Joel Nothman	\N	\N	\N	\N	\N	\N		In this paper we propose an unsupervised approach for acquiring domain related conceptual hierarchies from open-domain text. Super Sense Tagging (SST) is used to extract up-level terms and Wikipedia categories and WordNet are employed to construct the rest of taxonomic hierarchy. The result is a complete top-bottom taxonomy for every formal context. We describe both the method we implemented and some encoruaging initial experimental results.	78a50f38f3718963c9d95b78a0ac3a79	a1925ff0affb50314e47d6ba57c8f0c8	3f9d976f3ae6ded8dc32205e1aa7b87c	inproceedings	Using Wikipedia and supersense tagging for semi-automatic complex
\
taxonomy construction	Davide Picca and Adrian Popescu	\N	2007
7140969	International Symposium on Wikis: Proceedings of the 2005 international symposium on Wikis	16	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	desilets2005wu	http://iit-iti.nrc-cnrc.gc.ca/iit-publications-iti/docs/NRC-48272.pdf	\N		\N	\N	3--15	\N	18	\N		Wikis are simple to use, asynchronous, Web-based collaborative hypertext authoring systems which are quickly gaining in popularity. In spite of much anecdotal evidence to the effect that wikis are usable by non technical experts, this has never been studied formally. In this paper, we studied the usability of a wiki through observation and problem-solving interaction with several children who used the tool to collaboratively author hypertext stories over several sessions. The children received a minimal amount of instruction, but were able to ask for help during their work sessions. Despite minimal instruction, 5 out of 6 teams were able to complete their story. Our data indicate that the major
\
usability problems were related to hyperlink management. We report on this and other usability issues, and provide suggestions for improving the usability of wikis. Our analysis and conclusions also apply to hypertext authoring with non wiki-based tools.	ad6b4d33de60cb99df1b26ac2ab2fba0	d594c358a53de54167fed186e610ccae	021c0c8356fa40d4aca7244318a2c265	article	{Are wikis usable?}	A. Desilets and S. Paquet and N.G. Vinson	\N	2005
7140989	\N	\N	\N	\N	\N	\N	\N	\N	School of Engineering, University of California	\N	\N	Santa Cruz, CA, USA	\N	\N	Alfaro2007atw	http://www.soe.ucsc.edu/~luca/papers/07/trust-techrep.pdf	\N		\N	\N	\N	\N	UCSC-CRL-07-09	\N		The Wikipedia is a collaborative encyclopedia: anyone can contribute to its articles simply by clicking on an ``edit'' button. The open nature of the Wikipedia has been key to its success, but has also created a challenge: how can readers form an informed opinion on its reliability? We propose a system that computes quantitative values of trust for the text in Wikipedia articles; these trust values provide an indication of text reliability.
\

\
The system uses as input the revision history of each article, as well as information about the reputation of the contributing authors, as provided by a reputation system. The trust of a word in an article is computed on the basis of the reputation of the original author of the word, as well as the reputation of all authors who edited the text in proximity of the word. The algorithm computes word trust values that vary smoothly across the text; the trust values can be visualized using varying text-background colors. The algorithm ensures that all changes to an article text are reflected in the trust values, preventing surreptitious content changes.
\

\
We have implemented the proposed system, and we have used it to compute and display the trust of the text of thousands of articles of the English Wikipedia. To validate our trust-computation algorithms, we show that text labeled as low-trust has a significantly higher probability of being edited in the future than text labeled as high-trust. Anecdotal evidence seems to corroborate this validation: in practice, readers find the trust information valuable. 	a1ce201c36c215476fa5a7f28b16e6ce	814327db44ee6a212dfa195b1d2c8a0d	f0a6f5f09f02276c6090e894cba1779b	techreport	Assigning Trust To Wikipedia Content	B.T. Adler and J. Benterou and K. Chatterjee and L. de Alfaro and I. Pye and V. Raman	\N	2007
7141947	\N	4425	\N	\N	\N	\N	ECIR	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	Metzler07	http://dblp.uni-trier.de/db/conf/ecir/ecir2007.html#MetzlerDM07	\N	dblp	\N	\N	16-27	\N	\N	conf/ecir/2007	date = {2007-06-06}, ee = {http://dx.doi.org/10.1007/978-3-540-71496-5_5}, isbn = {978-3-540-71494-1}	\N	5cfd3270d29be8d7cc10cdd5fcb7a12b	540e1a7e8d5c6be019e3ec9b0f1c964d	cc7bdbcfc203c46ec90c2f6ca964c750	inproceedings	Similarity Measures for Short Segments of Text.	Donald Metzler and Susan T. Dumais and Christopher Meek	Giambattista Amati and Claudio Carpineto and Giovanni Romano	2007
7141959	\N	\N	\N	\N	Septemberand 22-24	\N	Proc. of the 8th International Conference on Knowledge-Based Intelligent \	Information and Engineering Systems (KES-2004) - Wellingtonand New \	Zealand	\N	\N	\N	Springer-Verlag	\N	\N	\N	Liu04a	\N	\N		\N	\N	\N	\N	\N	\N		\N	7b5fb8ae3092e868664a0c2cd2e7dccf	1f702eee80bf05be1291d63e4f83723d	c63ede8a0f68b7e9a970a40ee2730c71	inproceedings	{Commonsense reasoning in and over natural language}	H. Liu and P. Singh	M. Negoitaand R. J. Howlett and L. C. Jain	2004
7141996	\N	\N	\N	\N	\N	\N	ACL 2005, 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 25-30 June 2005, University of Michigan, USA	\N	\N	\N	The Association for Computational Linguistics	\N	\N	\N	tatu2005	\N	\N		\N	\N	\N	\N	\N	DBLP:conf/acl/2005	ee = {http://acl.ldc.upenn.edu/P/P05/P05-2006.pdf}, bibsource = {DBLP, http://dblp.uni-trier.de}	\N	b02f5570a20c48dbfebb2b5df85eb3a5	1f7ede1cb08b134249269b97edc1b7df	cd151718c0f49157c2ffff3989209f73	inproceedings	Automatic Discovery of Intentions in Text and its Application to Question Answering	M. Tatu	\N	2005
7142719	\N	4519	\N	\N	July	\N	Proceedings of the European Semantic Web Conference (ESWC2007)	\N	\N	\N	Springer-Verlag	Berlin Heidelberg, Germany	\N	LNCS	motta07eswc	http://www.eswc2007.org/pdf/eswc07-specia.pdf	\N		\N	\N	624-639	\N	\N	\N		While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the Del.icio.us and Flickr tag sets showed that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies, and meaningful relationships among subsets of these tags can be identified.	42dba18de3f2a89afca711506a7522fe	b828fbd5c9ddc4f9551f973445ecb283	35842f5deb96573cc24e818697d4bbc8	inproceedings	Integrating Folksonomies with the Semantic Web	Lucia Specia and Enrico Motta	Enrico Franconi and Michael Kifer and Wolfgang May	2007
7146253	\N	\N	\N	\N	\N	\N	WSDM '08: Proceedings of the international conference on Web search and web data mining	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Heymann2008	\N	\N		\N	\N	195--206	\N	\N	\N		\N	f6a150b3412f1ad32a01deec6a3fef52	3192b26a3b1394e24283766de46dc14b	7ffee89349e08beef1b55ab9d68ddd30	inproceedings	Can social bookmarking improve web search?	Paul Heymann and Georgia Koutrika and Hector Garcia-Molina	\N	2008
7146841	\N	\N	\N	\N	\N	\N	WSDM '08: Proceedings of the international conference on Web search and web data mining	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Heymann2008	\N	\N		\N	\N	195--206	\N	\N	\N		\N	f6a150b3412f1ad32a01deec6a3fef52	3192b26a3b1394e24283766de46dc14b	7ffee89349e08beef1b55ab9d68ddd30	inproceedings	Can social bookmarking improve web search?	Paul Heymann and Georgia Koutrika and Hector Garcia-Molina	\N	2008
7162243	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Ergon	\N	\N	Content and Communication. Terminology, Language Resources and Semantic Iteroperability	bertram2005	\N	\N		\N	\N	\N	\N	\N	\N		\N	fc5b908a2cdf64bea34458fd50799307	253136e82e1cb8670ffec011bd3f1026	3783103f0e62b116cccc935109a76725	article	Einführung in die inhaltliche Erschließung
\
Grundlagen - Methoden - Instrumente	Jutta Bertram	\N	2005
7170420	\N	\N	\N	\N	May	\N	Proc. Intl. Conf. on the Design of Cooperative Systems 2008	\N	\N	\N	Institut d'Etudes Politiques d'Aix-en-Provence	Carry-le-Rouet, France	\N	\N	RiKoCoop08	http://www.kooperationssysteme.de/wordpress/wp-content/uploads/coop08_richterkoch_functions_of_social_networking_services_final.pdf	\N	english version	\N	\N	87-98	\N	\N	\N		\N	9f39745894de22769ccc197c63b3b396	2a28222ed19a545f567cfc067a46ba8e	6223b5d4789fb62de9d14362e49d3c93	inproceedings	Functions of Social Networking Services	Alexander Richter and Michael Koch	Parina Hassanaly and Athissingh Ramrajsingh and Dave Randall and Paascal Salembier and Mattheu Tixier	2008
7170573	Journal of Computer-Mediated Communication, 13(1), article 11	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	BoEl07	http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html	\N	Social network sites(SNSs)are increasingly attracting the attention of academic and industry researchers intrigued by their affordances and reach. This special theme section of the Journal of Computer-Mediated Communication brings together scholarship on these emergent phenomena. In this introductory article, we describe features of SNSs and propose a comprehensive definition. We then present one perspective on the history of such sites, discussing key changes and developments. After briefly summarizing existing scholarship concerning SNSs, we discuss the articles in this special section and conclude with considerations for future research	\N	\N	\N	\N	\N	\N		\N	75f465399fcac1ad28750303fc6aa1c3	d86d8d93ce52a5bb5ed01f11a609bd99	f8139b0d83575e6cab1a0bee208c9cb2	article	Social Network Sites: Definition, History, and Scholarship	Danah M. Boyd and Nicole B. Ellison	\N	2007
7172805	\N	\N	\N	\N	June	\N	The Semantic Web: Research and Applications	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	hoser2006semantic	\N	\N		\N	Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro	\N	\N	\N	\N		A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very
\
 few SNA applications, and virtually none for analyzing the
\
 structure of ontologies.
\

\
We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.	00cb45dd4a5258f7b9d152018ce8f26d	344ec3b4ee8af1a2c6b86efc14917fa9	9a2c77c7c7a1b19cd16df08cca65f706	inproceedings	Semantic Network Analysis of Ontologies	Bettina Hoser and Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme	\N	2006
7176208	\N	\N	\N	\N	\N	\N	SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval	\N	\N	\N	ACM	New York, NY, USA	\N	\N	heymann2008social	http://portal.acm.org/citation.cfm?id=1390334.1390425	\N	Social tag prediction	\N	\N	531--538	\N	\N	\N	location = {Singapore, Singapore}, isbn = {978-1-60558-164-4}, doi = {http://doi.acm.org/10.1145/1390334.1390425}	In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.	84429a4ff568f0491176840ccd9f78f8	bb9455c80cc9bd8cf95c951a1318dabc	0e6023e192f539fe4fce9894b1fbca5a	inproceedings	Social tag prediction	Paul Heymann and Daniel Ramage and Hector Garcia-Molina	\N	2008
7181146	\N	\N	\N	\N	\N	\N	CHI '07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems	\N	\N	\N	ACM	New York, NY, USA	\N	\N	1240823	http://portal.acm.org/citation.cfm?doid=1240624.1240823	\N	FASTDash	\N	\N	1313--1322	\N	\N	\N	location = {San Jose, California, USA}, isbn = {978-1-59593-593-9}, doi = {http://doi.acm.org/10.1145/1240624.1240823}	\N	477f6f548ae949050cd8feeedfdfdaa0	8f3ed3f673f9b1d57b22443d772bd0f1	e640c97abfe8304b5734d9307541d4c0	inproceedings	{FASTDash}: a visual dashboard for fostering awareness in software teams	Jacob T. Biehl and Mary Czerwinski and Greg Smith and George G. Robertson	\N	2007
7181152	\N	\N	\N	\N	\N	\N	CSCW '02: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	587122	http://portal.acm.org/citation.cfm?id=587122&dl=GUIDE&coll=GUIDE&CFID=18264210&CFTOKEN=30940399	\N	Designing and deploying an information awareness interface	\N	\N	314--323	\N	\N	\N	location = {New Orleans, Louisiana, USA}, isbn = {1-58113-560-2}, doi = {http://doi.acm.org/10.1145/587078.587122}	The concept of awareness has received increasing attention over the past several CSCW conferences. Although many awareness interfaces have been designed and studied, most have been limited deployments of research prototypes. In this paper we describe Sideshow, a peripheral awareness interface that was rapidly adopted by thousands of people in our company. Sideshow provides regularly updated peripheral awareness of a broad range of information from virtually any accessible web site or database. We discuss Sideshow's design and the experience of refining and redesigning the interface based on feedback from a rapidly expanding user community.	3727b0098eb63cd52a17465e30f5ce83	83a36355477941fea1e6c03bf888a1af	4c62f30f6abb2605936d1e0ed811558d	inproceedings	Designing and deploying an information awareness interface	J. J. Cadiz and Gina Venolia and Gavin Jancke and Anoop Gupta	\N	2002
7181195	\N	\N	\N	\N	\N	\N	CSCW '92: Proceedings of the 1992 ACM Conference on Computer Supported Cooperative Work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	143468	http://portal.acm.org/citation.cfm?doid=143457.143468	\N	Awareness and coordination in shared workspaces	\N	\N	107--114	\N	\N	\N	location = {Toronto, Ontario, Canada}, isbn = {0-89791-542-9}, doi = {http://doi.acm.org/10.1145/143457.143468}	\N	08bd23df9ddf0c756ffb81051fb72d58	5371d4ed0e85124fa97d3b2c97d4c6c0	e83c9846499f30b0f8f802fc0ff91c7e	inproceedings	Awareness and coordination in shared workspaces	Paul Dourish and Victoria Bellotti	\N	1992
7207567	Journal of Mathematical Sociology	25	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	brandes2001betweeness	\N	\N	Main paper DB	\N	\N	\N	\N	163	\N	date-added = {2007-01-22 11:56:01 -0800}, date-modified = {2007-11-13 18:08:47 -0500}, local-url = {brandes/2001-a faster.pdf}	\N	6710ef87b6911a81bf76a2135fbf432c	dff547c1a86412c8f3742aab68f7a243	6bd362e178e17085f4cc30eebf38a43e	article	A faster algorithm for betweenness centrality	Ulrik Brandes	\N	2001
7218059	Computational Linguists	32	\N	\N	\N	\N	\N	\N	\N	\N	MIT Press	Cambridge, MA, USA	\N	\N	Budanitsky2006wordnet	http://ftp.cs.toronto.edu/pub/gh/Budanitsky+Hirst-2006.pdf	\N		\N	\N	13--47	\N	1	\N		\N	c5a5fef4e1ed5e84b9bbb616c1ec808e	a259f21d89bdc61a64ce11a3aea0af06	563138e890f4463f29c0324c95878129	article	Evaluating WordNet-based Measures of Lexical Semantic Relatedness	Alexander Budanitsky and Graeme Hirst	\N	2006
7232177	\N	\N	\N	\N	\N	\N	WWW '05: Proceedings of the 14th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Ziegler05	http://portal.acm.org/citation.cfm?id=1060754	\N	Improving recommendation lists through topic diversification	\N	\N	22--32	\N	\N	\N	location = {Chiba, Japan}, isbn = {1-59593-046-9}, doi = {http://doi.acm.org/10.1145/1060745.1060754}	In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.	c41fc1315e2e0821d34f356de26646aa	0a7f89e65c4a0a5e45aa69a54a5600e6	8c44d8e6f51364e3133b82771ce0f4a5	inproceedings	Improving recommendation lists through topic diversification	Cai-Nicolas Ziegler and Sean M. McNee and Joseph A. Konstan and Georg Lausen	\N	2005
7235179	\N	\N	\N	\N	\N	\N	SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data	\N	\N	\N	ACM Press	New York, NY, USA	\N	\N	agrawal93association	http://cs.sungshin.ac.kr/~jpark/HOME/References/agrawal_sigmod93.ps	\N	Mining association rules between sets of items in large databases	\N	\N	207--216	\N	\N	\N	location = {Washington, D.C., United States}, isbn = {0-89791-592-5}, doi = {http://doi.acm.org/10.1145/170035.170072}	\N	986b432f9d55bd47ac154a93d9459829	51138a142393790fdda4482573812d63	c471f1ca660f1beebd4cc79787180127	inproceedings	Mining association rules between sets of items in large databases	Rakesh Agrawal and Tomasz Imieli\\&\\#324;ski and Arun Swami	\N	1993
7245250	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Prentice-Hall	\N	\N	\N	russell95ai	\N	\N	BibSonomy::bibtex::Artificial {I}ntelligence: {A} modern approach	\N	\N	\N	\N	\N	\N	language = {english}	\N	524375a8616639d49082d23e426df1e7	2147f6fa2e4b18c82b9ab9c06f5c1bca	caa9594a843949b73e9ba3fbb6db1ee5	book	Artificial {I}ntelligence: {A} modern approach	S. Russell and P. Norvig	\N	1995
7245813	Journal of Artificial Intelligence Research (JAIR)	24	\N	\N	\N	\N	\N	\N	\N	\N	AAAI Press	\N	\N	\N	cimiano05evaluation	http://www.jair.org/media/1648/live-1648-2403-jair.pdf	\N		\N	\N	305-339	\N	\N	\N	issn = {1076-9757}, vgwort = {54}	\N	92686192e2ed47f02916891795a0c040	4c09568cff62babd362aab03095f4589	2d7c9ea5484ee45ea8bf3520138d7477	article	Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis	P. Cimiano and A. Hotho and S. Staab	\N	2005
7246830	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Strube2006	http://www.dit.unitn.it/~p2p/RelatedWork/Matching/aaai06.pdf	\N		\N	\N	\N	\N	\N	\N		\N	0759fa442899ab779f60067037df57ea	a09d5123ab9ab8cb00b8df6f0a7f5c81	0a2373a9ba1965fca60caee6cb97d121	inproceedings	WikiRelate! Computing Semantic Relatedness Using Wikipedia	M. Strube and S. P. Ponzetto	\N	2006
7250148	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	ORe05	http://oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html	\N		\N	\N	\N	\N	\N	\N		\N	641c279d863fe843870007473fdd7cd5	cfb82fcad7e01c3ed732288cf9042c55	f9397713480f0b81d5e8a0effb867a13	article	What Is Web 2.0? Design Patterns and Business Models for the Next Generation of Software.	Tim O'Reilly	\N	2005
7252879	\N	\N	\N	\N	\N	\N	Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures	\N	\N	\N	\N	\N	\N	\N	Hotho2006	\N	\N	web2.0 papers	\N	to appear	\N	\N	\N	\N		\N	20579fa83afd7f3573d072fc4ba52714	d28c9f535d0f24eadb9d342168836199	a6faae355d867b5f998ed30e79eec3cf	inproceedings	{BibSonomy}: A Social Bookmark and Publication Sharing System	Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme	\N	2006
7252929	\N	\N	\N	\N	May	\N	Proceedings of the WWW 2006 Workshop on Collaborative Web Tagging  Workshop	\N	\N	\N	\N	Edinburgh	\N	\N	Begelman2006	http://www.rawsugar.com/www2006/taggingworkshopschedule.html	\N	automated tag clustering	\N	\N	\N	\N	\N	\N	pdf = {http://www.rawsugar.com/www2006/20.pdf}	\N	882669a1264ab98f6e6ca9ff98d2bf94	ffacd9d40f6cba1aa8140f501c2a1802	95449b3d4b12e8930d529e1e22d51e04	inproceedings	Automated Tag Clustering: Improving search and exploration in the tag space	Grigory Begelman and Philipp Keller and Frank Smadja	\N	2006
7252930	\N	\N	\N	\N	\N	\N	Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining	\N	\N	\N	ACM Press	\N	\N	\N	Beil2002	\N	\N		\N	\N	436--442	\N	\N	\N	location = {Edmonton, Alberta, Canada}, isbn = {1-58113-567-X}, doi = {http://doi.acm.org/10.1145/775047.775110}	\N	96f62c55f6266d2735f6678d49232b2d	afbbcb8e9e77abf0eca425048f104a51	e8a9409756b28009f0ca0fb4d8240d57	inproceedings	Frequent term-based text clustering	Florian Beil and Martin Ester and Xiaowei Xu	\N	2002
7252931	\N	\N	\N	\N	\N	\N	SIGIR	\N	\N	\N	\N	\N	\N	\N	Buckley1985	http://dblp.uni-trier.de/db/conf/sigir/sigir85.html#BuckleyL85	\N	applicationan efficient way of finding the nearest neighbors of a document	\N	\N	97-110	\N	\N	\N	ee = {http://doi.acm.org/10.1145/345508.345618}	\N	cccd4d52d943e3482776484740866d73	0514e85275d5c6679fce59245904dcdc	4cbbeca9b1c13317faff32d0b64a016a	inproceedings	Optimization of Inverted Vector Searches.	Chris Buckley and A. F. Lewit	\N	1985
7252934	AI Communications	20	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cattuto2007	http://www.kde.cs.uni-kassel.de/hotho/pub/2007/aicomm_2007_folksonomy_clustering.pdf	\N		\N	\N	245 - 262	\N	4	\N	vgwort = {67}	\N	d70721ce8ec7ffd8dcc8975c0a2c9fc8	fc5f2df61d28bc99b7e15029da125588	d87e198a6d564ae8a8fe151e0a96fa0f	article	Network Properties of Folksonomies	C. Cattuto and C. Schmitz and A. Baldassarri and V. D. P. Servedio and V. Loreto and A. Hotho and M. Grahl and G. Stumme	\N	2007
7252939	\N	\N	\N	\N	September	\N	\N	Paperback	\N	\N	Springer	\N	\N	\N	Ester2000	http://www.amazon.fr/exec/obidos/ASIN/3540673288/citeulike04-21	\N		\N	\N	\N	\N	\N	\N	isbn = {3540673288}	\N	68cd9d7832899a021a529f7fbdf99c35	ce70e6261519f27b6a1b4e627991f713	598d5d86ef03c20bbded8410f9558eed	book	Knowledge Discovery in Databases : Techniken und Anwendungen	Martin Ester and Jörg Sander	\N	2000
7252943	PNAS	99	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Girvan2002	\N	\N		\N	\N	7821-7826	\N	12	\N		\N	0623d7c9af0ac7d6d8d0f73672d2c793	ecd7a48a37f660ab421472140168c892	b8b5ab1843c2dde870fd02141ffcd810	article	Community structure in social and biological networks	M. Girvan and M. E. J. Newman	Lawrence A. Shepp	2002
7252945	\N	\N	\N	\N	sep	\N	Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)	\N	\N	\N	Martin-Luther-Universität Halle-Wittenberg	\N	\N	\N	Grahl2007	http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf 	\N		\N	\N	50-54	\N	\N	\N	isbn = {978-3-86010-907-6}, vgwort = {14}	\N	73709a1929b98e9d33558558e6077c0c	9c3bb05456bf11bcd88a1135de51f7d9	6d5188d66564fe4ed7386e28868504de	inproceedings	Conceptual Clustering of Social Bookmark Sites	Miranda Grahl and Andreas Hotho and Gerd Stumme	Alexander Hinneburg	2007
7252952	\N	286	\N	\N	\N	\N	\N	\N	\N	\N	Akademische Verlagsgesellschaft Aka GmbH	Berlin	\N	Diski	Hotho2004	http://www.kde.cs.uni-kassel.de/hotho/pub/2004/dissAho.pdf	\N		\N	\N	\N	\N	\N	\N	isbn = {3-89838-286-9}	\N	83ac5435a4633eefa11eec405995211a	174d464d8c6c38b690ab8aa76cd3fe5f	fc7f40f7b7c8e3f72acb881b6d2d2680	book	Clustern mit Hintergrundwissen	Andreas Hotho	\N	2004
7252955	\N	\N	\N	\N	August	\N	Proceedings of the Tenth Workshop on Web Mining and Web Usage Analysis (WebKDD)	\N	\N	\N	ACM	\N	\N	\N	Java2008	http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies	\N		\N	Held in conjunction with The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008)	\N	\N	\N	\N		We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information that is readily available in most social media systems. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently to within this set than outside it and they share similar tags. Our technique is based on the Normalized Cut (NCut) algorithm and can be easily and efficiently implemented. We validate our method by using a real network of blogs and tag information obtained from a social bookmarking site. We also verify our results on a citation network for which we have access to ground truth cluster information. Our method, Simultaneous Cut (SimCut), has the advantage that it can group related tags and cluster the nodes simultaneously.	074c14fe5113292ba62714a15cf47651	acfec953843b168e61e2e167e29b4c3d	80e4f692625afcf21e8195915ea93de0	inproceedings	Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies	Akshay Java and Anupam Joshi and Tim Finin	\N	2008
7252956	\N	\N	\N	\N	August	\N	WebKDD 2008 Workshop on Web Mining and Web Usage Analysis	\N	\N	\N	\N	\N	\N	\N	Java2008a	\N	\N		\N	To Appear	\N	\N	\N	\N		\N	bf02472ea5d7e6faaae365bb8a88aa30	acfec953843b168e61e2e167e29b4c3d	645abd6b3191a2a6e844d7542651ed1c	inproceedings	Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies	Akshay Java and Anupam Joshi and Tim Finin	\N	2008
7252961	\N	\N	\N	\N	\N	\N	Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability - Vol. 1	\N	\N	\N	University of California Press, Berkeley, CA, USA	\N	\N	\N	MacQueen1967a	http://projecteuclid.org/euclid.bsmsp/1200512992	\N		\N	\N	281--297	\N	\N	\N		\N	decb3581278889d4e7c4a37d3bda88d0	8d7d4dfe7d3a06b8c9c3c2bb7aa91e28	d23dfdff44ca5121fde221604128ab80	inproceedings	Some Methods for Classification and Analysis of Multivariate Observations	J. MacQueen	L. M. {Le Cam} and J. Neyman	1967
7252962	University of California Press	\N	\N	\N	\N	\N	Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability	\N	\N	\N	\N	\N	\N	\N	MacQueen1967	http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html#macqueen	\N		\N	\N	281-297	\N	\N	\N	date = {(1967):}	\N	51c5b9ec2f1f429435d624ddcd1d5f46	8d7d4dfe7d3a06b8c9c3c2bb7aa91e28	55b2985db0b65d237559f6431dfded58	inproceedings	Some Methods for Classification and Analysis of Multivariate Observations	J. B. MacQueen	\N	1967
7252964	Proceedings of the National Academy of Sciences	103	\N	\N	\N	\N	\N	\N	\N	\N	National Acad Sciences	\N	\N	\N	Newman2006	\N	\N		\N	\N	8577--8582	\N	23	\N		\N	90113c14405f0b5991d9719fdc23649f	e664336d414a1e21d89f30cc56f5e739	9104cb1678a39c96b06ed838a8aa3a63	article	Modularity and community structure in networks	MEJ Newman	\N	2006
7252974	\N	4519	\N	\N	July	\N	Proceedings of the European Semantic Web Conference (ESWC2007)	\N	\N	\N	Springer-Verlag	Berlin Heidelberg, Germany	\N	LNCS	Specia2007	http://www.eswc2007.org/pdf/eswc07-specia.pdf	\N		\N	\N	624-639	\N	\N	\N		While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the Del.icio.us and Flickr tag sets showed that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies, and meaningful relationships among subsets of these tags can be identified.	42dba18de3f2a89afca711506a7522fe	b828fbd5c9ddc4f9551f973445ecb283	35842f5deb96573cc24e818697d4bbc8	inproceedings	Integrating Folksonomies with the Semantic Web	Lucia Specia and Enrico Motta	Enrico Franconi and Michael Kifer and Wolfgang May	2007
7264261	\N	\N	\N	\N	\N	\N	\N	Online	\N	\N	\N	\N	\N	\N	Noy2001	http://www.ksl.stanford.edu/people/dlm/papers/ontology101/ontology101-noy-mcguinness.html	\N		\N	\N	\N	\N	\N	\N		\N	39236ccb2633482251d20aac7ecda7ac	2a496bb63d728d96264a50c0a4960e9a	dc68dbc549f9f8e4c54b20ca0224531f	misc	Ontology Development 101: A Guide to Creating Your First Ontology	Natalya F. Noy and Deborah L. {mcguinness}	\N	2001
7268089	\N	\N	\N	\N	\N	\N	CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	1180901	http://portal.acm.org/citation.cfm?id=1180875.1180901	\N	Study: Facebook is not used for social networking but to grab news about friends	\N	\N	167--170	\N	\N	\N	location = {Banff, Alberta, Canada}, isbn = {1-59593-249-6}, doi = {http://doi.acm.org/10.1145/1180875.1180901}	Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.	6d8f1c4a844dc8efb365f5f759193114	938df95e9223d4b24e2ea738e6cf6f44	1d806b573191a6ebc47d44f6c9cf859f	inproceedings	A face(book) in the crowd: social Searching vs. social browsing	Cliff Lampe and Nicole Ellison and Charles Steinfield	\N	2006
7268104	\N	\N	\N	\N	\N	\N	CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	lampe2006facebook	http://portal.acm.org/citation.cfm?id=1180875.1180901	\N	Study: Facebook is not used for social networking but to grab news about friends	\N	\N	167--170	\N	\N	\N	location = {Banff, Alberta, Canada}, isbn = {1-59593-249-6}, doi = {http://doi.acm.org/10.1145/1180875.1180901}	Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.	6d8f1c4a844dc8efb365f5f759193114	938df95e9223d4b24e2ea738e6cf6f44	1d806b573191a6ebc47d44f6c9cf859f	inproceedings	A face(book) in the crowd: social Searching vs. social browsing	Cliff Lampe and Nicole Ellison and Charles Steinfield	\N	2006
7269866	\N	\N	\N	\N	\N	\N	KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining	\N	\N	\N	ACM Press	New York, NY, USA	\N	\N	347121	\N	\N		\N	\N	150--160	\N	\N	\N	location = {Boston, Massachusetts, United States}, isbn = {1-58113-233-6}, doi = {http://doi.acm.org/10.1145/347090.347121}	\N	8ec6f86244132b345d8a4b51759b4143	e74be2040258b24f3b2e03466931a9da	b37bffe4a02dace7c303d663fd24182c	inproceedings	Efficient identification of Web communities	Gary William Flake and Steve Lawrence and C. Lee Giles	\N	2000
7276517	Knowledge and Information Systems	6	\N	\N	#jul#	\N	\N	\N	\N	\N	\N	\N	\N	\N	PM04	\N	\N		\N	\N	441--464	\N	4	\N		Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.
\
ER  -	9b651eaf021234b9a7023d8c2d3e177e	2d8d29b1a5ebf0b43d8d50091cf1acbf	98dad48daec5af330aa3408ae8e230fd	article	Ontologies: How can They be Built?	Helena Sofia Pinto and João P. Martins	\N	2004
7276882	Social Semantic Web	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Hotho2009	http://dx.doi.org/10.1007/978-3-540-72216-8_18	\N		\N	\N	363--391	\N	\N	\N		BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung
\
der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der inBibSonomy und ähnlichen Systemen enthaltenen Daten ein.	e8f9b15ae25673063b53b5db0442913a	79dbca4289cfe913aa7f7eb7e0dccea7	c606b381f94689122d3ad0bc3c3ef723	article	Social Bookmarking am Beispiel BibSonomy	Andreas Hotho and Robert Jäschke and Dominik Benz and Miranda Grahl and Beate Krause and Christoph Schmitz and Gerd Stumme	\N	2009
7286591	\N	\N	\N	\N	\N	\N	JCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Yanbe07	http://portal.acm.org/citation.cfm?id=1255175.1255198	\N	Can social bookmarking enhance search in the web?	\N	\N	107--116	\N	\N	\N	location = {Vancouver, BC, Canada}, isbn = {978-1-59593-644-8}, doi = {http://doi.acm.org/10.1145/1255175.1255198}	\N	2af93097f95b2e6c67f468bb0425eb81	13ebfc0942b5908890c3caaa7046fe50	d896ae22bc7b52edefbfb9cdb373cf83	inproceedings	Can social bookmarking enhance search in the web?	Yusuke Yanbe and Adam Jatowt and Satoshi Nakamura and Katsumi Tanaka	\N	2007
7286597	\N	4956	\N	\N	April	\N	30th European Conference on IR Research, ECIR 2008	\N	\N	\N	Springer	Glasgow, UK	\N	Lecture Notes in Computer Science	Krause08	\N	\N		\N	\N	101-113	\N	\N	\N	misc = {vgwort=21,1}, isbn = {978-3-540-78645-0}	\N	7ee418bbed426a12581861fde4d6c360	37598733b747093d97a0840a11beebf5	ad5f4664d1f5b967a0793b4a26a0edbb	inproceedings	A Comparison of Social Bookmarking with Traditional Search	Beate Krause and Andreas Hotho and Gerd Stumme	Craig Macdonald and Iadh Ounis and Vassilis Plachouras and Ian Ruthven and Ryen W. White	2008
7300646	Phys Rev Lett	89	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Newman:2002p628	\N	\N		\N	\N	208701	\N	20	\N	date-added = {2008-05-29 18:11:01 +0200}, local-url = {file://localhost/Users/sven/Documents/Papers/2002/Newman/Phys%20Rev%20Lett%202002%20Newman.pdf}, uri = {papers://B7B184F3-8CE5-4C43-B61C-B7952DE67982/Paper/p628}, date-modified = {2008-08-21 20:08:45 +0200}, read = {Yes}, rating = {0}	A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.	008d0cbf73fe1bd4994301477d85bd19	7265c6dc287861591f52e46b17404a08	98905303b4b2225862dec1f0fb9b8464	article	Assortative Mixing in Networks	M Newman	\N	2002
7300648	Phys Rev Lett	86	\N	\N	Jan	\N	\N	\N	\N	\N	\N	\N	\N	\N	PastorSatorras:2001p7012	http://link.aps.org/doi/10.1103/PhysRevLett.86.3200	\N		\N	\N	3200--3203	\N	14	\N	date-added = {2008-12-03 13:32:40 +0100}, pmid = {15764887746584984620}, local-url = {file://localhost/Users/sven/Documents/Papers/2001/Pastor-Satorras/Phys%20Rev%20Lett%202001%20Pastor-Satorras.pdf}, uri = {papers://B7B184F3-8CE5-4C43-B61C-B7952DE67982/Paper/p7012}, date-modified = {2008-12-03 13:33:25 +0100}, rating = {0}	The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.	244029c59773b53e4a52c13e75f30ee8	8d497708aa329c5969f15e5bcd9f38f4	90597f9367f3bfd80a7fe96f92679784	article	Epidemic Spreading in Scale-Free Networks	R Pastor-Satorras and A Vespignani	\N	2001
7300652	P Natl Acad Sci USA	99	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Girvan:2002p590	\N	\N		\N	\N	7821--7826	\N	12	\N	date-added = {2008-05-29 18:14:49 +0200}, local-url = {file://localhost/Users/sven/Documents/Papers/2002/Girvan/P%20Natl%20Acad%20Sci%20USA%202002%20Girvan.pdf}, uri = {papers://B7B184F3-8CE5-4C43-B61C-B7952DE67982/Paper/p590}, date-modified = {2008-08-08 13:11:49 +0200}, read = {Yes}, rating = {0}	A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.	7e6f4b3d959e107dbeac2a094513ce44	ecd7a48a37f660ab421472140168c892	b8b322cbba20e1e3ec6fa95cc754e3a6	article	Community structure in social and biological networks	M Girvan and M Newman	\N	2002
7300785	Sci Am	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Barabasi:2003p3633	\N	\N		\N	\N	\N	\N	\N	\N	date-modified = {2008-08-08 18:09:25 +0200}, local-url = {file://localhost/Users/sven/Documents/Papers/2003/Barab%C3%A1si/Sci%20Am%202003%20Barab%C3%A1si.pdf}, read = {Yes}, rating = {0}, uri = {papers://B7B184F3-8CE5-4C43-B61C-B7952DE67982/Paper/p3633}	\N	ea5016a2ed0af320c449d24c26c5fa20	a2779a7947795f0e0486c766efffbb47	42a5b50377795bca7c4e915c4936a29a	article	Scale-Free Networks	A Barab{\\'a}si and E Bonabeau	\N	2003
7305555	\N	\N	\N	\N	\N	\N	13th International Conference on Knowledge Engineering and Knowledge Management (EKAW02)	\N	\N	\N	\N	Siguenza, Spain	\N	\N	MnM02	http://kmi.open.ac.uk/projects/akt/publication-pdf/vargas-vera-etal.pdf	\N		\N	\N	379--391	\N	\N	\N		\N	2ceb1ea649c0639141422df280fdf73d	d2d3a8006510f1b96aedfdf3970e9920	267f288151632f63429ee34c744f35a2	inproceedings	{MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup}	M. Vargas-Vera and E. Motta and J. Domingue and M. Lanzoni and A. Stutt and F. Ciravegna	\N	2002
7323332	\N	\N	\N	Pap/Cdr	\N	\N	\N	\N	\N	\N	Addison-Wesley Longman, Amsterdam	\N	\N	\N	020171499X	http://www.amazon.de/Wiki-Way-Quick-Collaboration-Web/dp/020171499X%3FSubscriptionId%3D13CT5CVB80YFWJEPWS02%26tag%3Dws%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D020171499X	\N	\N	\N	\N	\N	\N	\N	\N	ean = {9780201714999}, asin = {020171499X}, description = {The Wiki Way. Quick Collaboration on the Web.: Quick…Amazon.de: Bo Leuf, Ward Cunningham: Englische Bücher}, isbn = {020171499X}, biburl = {http://www.bibsonomy.org/bibtex/20642a6035133fabeef07cc5d1009c340/anneba}, dewey = {005.72}	\N	46c622dd1bcf5bba9f9d2baaf51c5f59	7f9fb2b5bdcc9be84048552ed1ed6d04	d9da7e75a1285918869cd90d51e22a78	book	The Wiki Way: Quick Collaboration on the Web	Bo Leuf and Ward Cunningham	\N	2001
7323434	\N	\N	\N	\N	\N	\N	CSCW '92: Proceedings of the 1992 ACM conference on Computer-supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	143468	http://portal.acm.org/citation.cfm?doid=143457.143468	\N	\N	\N	\N	107--114	\N	\N	\N	location = {Toronto, Ontario, Canada}, description = {Awareness and coordination in shared workspaces}, isbn = {0-89791-542-9}, biburl = {http://www.bibsonomy.org/bibtex/2f99219ff757c0cb4be238658fa9c9581/anneba}, doi = {http://doi.acm.org/10.1145/143457.143468}	Awareness of individual and group activities is critical to successful collaboration and is commonly supported in CSCW systems by active, information generation mechanisms separate from the shared workspace. These mechanisms penalise information providers, presuppose relevance to the recipient, and make access difficult, We discuss a study of shared editor use which suggests that awareness information provided and exploited passively through the shared workspace, allows users to move smoothly between close and loose collaboration, and to assign and coordinate work dynamically. Passive awareness mechanisms promise effective support for collaboration requiring this sort of behaviour, whilst avoiding problems with active approaches.	4caf4eb0884117df77dfe78080406e08	5371d4ed0e85124fa97d3b2c97d4c6c0	f99219ff757c0cb4be238658fa9c9581	inproceedings	Awareness and coordination in shared workspaces	Paul Dourish and Victoria Bellotti	\N	1992
7327837	AI Communications	20	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cattuto2007	http://www.kde.cs.uni-kassel.de/hotho/pub/2007/aicomm_2007_folksonomy_clustering.pdf	\N		\N	\N	245 - 262	\N	4	\N	vgwort = {67}	\N	d70721ce8ec7ffd8dcc8975c0a2c9fc8	fc5f2df61d28bc99b7e15029da125588	d87e198a6d564ae8a8fe151e0a96fa0f	article	Network Properties of Folksonomies	C. Cattuto and C. Schmitz and A. Baldassarri and V. D. P. Servedio and V. Loreto and A. Hotho and M. Grahl and G. Stumme	\N	2007
7330003	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	omelayenko01ontologies	http://citeseer.ist.psu.edu/omelayenko01learning.html	\N	\N	\N	\N	\N	\N	\N	\N	misc = {priority = {3}, citeulike-article-id = {229554}}	The next generation of the Web, called Semantic Web, has to improve the Web with semantic (ontological) page annotations to enable knowledge-level querying and searches. Manual construction of these ontologies will require tremendous efforts that force future integration of machine learning with knowledge acquisition to enable highly automated ontology learning. In the paper we present the state of the-art in the field of ontology learning from the Web to see how it can contribute to the task...	b9d1b5403aece7eaae675759cd527435	011d45b904b02fdf1a65122d2832710b	b0c27cadd522f88a770b373b70ce3b1d	misc	Learning of ontologies for the Web: the analysis of existent approaches	B. Omelayenko	\N	2001
7330519	Social Semantic Web	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Hotho2009	http://dx.doi.org/10.1007/978-3-540-72216-8_18	\N		\N	\N	363--391	\N	\N	\N		BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung
\
der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der inBibSonomy und ähnlichen Systemen enthaltenen Daten ein.	e8f9b15ae25673063b53b5db0442913a	79dbca4289cfe913aa7f7eb7e0dccea7	c606b381f94689122d3ad0bc3c3ef723	article	Social Bookmarking am Beispiel BibSonomy	Andreas Hotho and Robert Jäschke and Dominik Benz and Miranda Grahl and Beate Krause and Christoph Schmitz and Gerd Stumme	\N	2009
7331556	Bibliothek. Forschung und Praxis	31	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	regulski_2007	http://www.bibliothek-saur.de/2007_2/177-184.pdf	\N		\N	\N	177-184	\N	2	\N		\N	9210ab118261d6015f653920152abc73	21d0acb377c730344d85253cbc8025b9	1a361404f4ce786b33a45fce07e2c943	article	Aufwand und Nutzen beim Einsatz von Social-Bookmarking-Services als Nachweisinstrument für wissenschaftliche Forschungsartikel am Beispiel von BibSonomy. 	Katharina Regulski	\N	2007
7336505	SIGMIS Database	38	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	BrKo07	http://portal.acm.org/citation.cfm?doid=1314234.1314239	\N		\N	\N	17--25	\N	4	\N	issn = {0095-0033}, doi = {http://doi.acm.org/10.1145/1314234.1314239}	\N	c366ef11f8af915e94f92f0de0c4432b	8ba88d1a0ca8f7ff4ebd86e80a5c456e	eebaa012994d0fd3288485464f1fa9d4	article	Virtual worlds: multi-disciplinary research opportunities	David A. Bray and Benn R. Konsynski	\N	2007
7354658	\N	\N	\N	\N	\N	\N	HYPERTEXT '06: Proceedings of the seventeenth conference on Hypertext and hypermedia	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Marlow2006	http://portal.acm.org/citation.cfm?doid=1149941.1149949	\N	HT06, tagging paper, taxonomy, Flickr, academic article, to read	\N	\N	31--40	\N	\N	\N	location = {Odense, Denmark}, isbn = {1-59593-417-0}, doi = {http://doi.acm.org/10.1145/1149941.1149949}	In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., "tags") to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems.Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photo-sharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.	3f524c0cb68141a2ad8dbfd461137c86	3cd50bc064b9659829229f42eee284dd	c522d6982d34510925f7abbccfb29e14	inproceedings	HT06, tagging paper, taxonomy, Flickr, academic article, to read	Cameron Marlow and Mor Naaman and Danah Boyd and Marc Davis	\N	2006
7354756	\N	\N	\N	\N	\N	\N	Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007)	\N	\N	\N	\N	\N	\N	\N	Sood2007	http://icwsm.org/papers/2--Sood-Owsley-Hammond-Birnbaum.pdf	\N		\N	\N	\N	\N	\N	\N		In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion.
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\
Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.	ed60e66ab551735cf7096e6091b54beb	1a946aea1d46f79ad85c25add991bef6	bb8e778a61c315ad946dfec71b13cc8b	inproceedings	TagAssist: Automatic Tag Suggestion for Blog Posts	Sanjay Sood and Sara Owsley and Kristian Hammond and Larry Birnbaum	\N	2007
7355034	\N	\N	\N	\N	\N	\N	WWW '07: Proceedings of the 16th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Bao2007	http://portal.acm.org/citation.cfm?id=1242640	\N	Optimizing web search using social annotations	\N	\N	501--510	\N	\N	\N	location = {Banff, Alberta, Canada}, isbn = {978-1-59593-654-7}, doi = {http://doi.acm.org/10.1145/1242572.1242640}	\N	6a9d7f692cb34848565301082abbe4af	2cbdc7da88c90ef22468108c1f481159	b9966b9df0199a0b7b2d5a1b0d7560cb	inproceedings	Optimizing web search using social annotations	Shenghua Bao and Guirong Xue and Xiaoyuan Wu and Yong Yu and Ben Fei and Zhong Su	\N	2007
7357040	\N	\N	\N	\N	\N	\N	WWW '06: Proceedings of the 15th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Brooks2006	http://portal.acm.org/citation.cfm?id=1135869	\N	Improved annotation of the blogosphere via autotagging and hierarchical clustering	\N	\N	625--632	\N	\N	\N	location = {Edinburgh, Scotland}, isbn = {1-59593-323-9}, doi = {http://doi.acm.org/10.1145/1135777.1135869}	Tags have recently become popular as a means of annotating and organizing Web pages and blog entries. Advocates of tagging argue that the use of tags produces a 'folksonomy', a system in which the meaning of a tag is determined by its use among the community as a whole. We analyze the effectiveness of tags for classifying blog entries by gathering the top 350 tags from Technorati and measuring the similarity of all articles that share a tag. We find that tags are useful for grouping articles into broad categories, but less effective in indicating the particular content of an article. We then show that automatically extracting words deemed to be highly relevant can produce a more focused categorization of articles. We also show that clustering algorithms can be used to reconstruct a topical hierarchy among tags, and suggest that these approaches may be used to address some of the weaknesses in current tagging systems.	26ec8c81dac5ff31e0532590509e9f4f	c88a665abf8d88c5a7ae95fa2783f837	5c9c83e89da2faa8906a5927fe7ca3ef	inproceedings	Improved annotation of the blogosphere via autotagging and hierarchical clustering	Christopher H. Brooks and Nancy Montanez	\N	2006
7363402	\N	\N	\N	\N	\N	\N	SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Heymann2008	http://portal.acm.org/citation.cfm?id=1390334.1390425	\N	Social tag prediction	\N	\N	531--538	\N	\N	\N	location = {Singapore, Singapore}, isbn = {978-1-60558-164-4}, doi = {http://doi.acm.org/10.1145/1390334.1390425}	\N	b74f249dc0ec402c0de54b51b30f1204	bb9455c80cc9bd8cf95c951a1318dabc	f089aabc540f9bfaffffb0eddfbb5260	inproceedings	Social tag prediction	Paul Heymann and Daniel Ramage and Hector Garcia-Molina	\N	2008
7366228	Commun. ACM	49	\N	\N	April	\N	\N	\N	\N	\N	ACM Press	New York, NY, USA	\N	\N	Hearst2006Clustering	http://portal.acm.org/citation.cfm?id=1121949.1121983	\N		\N	\N	59--61	\N	4	\N	issn = {0001-0782}, citeulike-article-id = {577224}, priority = {3}, file = {Hearst2006Clustering.pdf:Hearst2006Clustering.pdf:PDF}, doi = {10.1145/1121949.1121983}	\N	9f3c5e426a23935df56b3c906915e903	85cbe3eb5c0b02339137fbb51650fbb2	84422aadd9c4aa0b8a06d7c62806a105	article	Clustering versus faceted categories for information exploration	Marti A. Hearst	\N	2006
7366304	\N	\N	\N	\N	\N	\N	IA Summit 2007	\N	\N	\N	\N	\N	\N	\N	Quintarelli2007Facetag	\N	\N		\N	\N	\N	\N	\N	\N	file = {Quintarelli2007Facetag.pdf:Quintarelli2007Facetag.pdf:PDF}	\N	19b0bf4542bba8f3216dc40125b338ff	0511d87c6527fa3eaa5b378673f40f91	04c75afb071b3e3cf4e05383e01f7dda	inproceedings	Facetag: Integrating Bottom-up and Top-down Classification in a Social\
\	Tagging System	E. Quintarelli and L. Rosati and A. Resmini	\N	2007
7367905	\N	\N	\N	\N	March	\N	\N	Hardcover	\N	\N	{McGraw-Hill Science/Engineering/Math}	\N	\N	\N	Mitchell1997	\N	\N		\N	\N	\N	\N	\N	\N	priority = {4}, isbn = {0070428077}	{This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.}	8e5bfc05bf23ff8c435900a9c53501f2	479a66c32badb3a455fbdcf8e6633a5d	3e79734ee1a6e49aee02ffd108224d1c	book	Machine Learning	Tom M. Mitchell	\N	1997
7381043	\N	\N	\N	\N	\N	\N	Handbook of Research on Web 2.0, 3.0 and X.0: Technologies, Business, and Social Applications	\N	\N	\N	IGI Global	\N	\N	\N	ZachariasBraunSchmidt09	\N	\N		\N	\N	\N	\N	\N	\N		\N	645137c2ba377f10f1f15c5311691bdf	2a603439eb8294c91c96f24314e3c288	bc78176cdb470c62b1a62100d257d9fe	incollection	Social Semantic Bookmarking with SOBOLEO	Valentin Zacharias and Simone Braun and Andreas Schmidt	San Murugesan	2009
7381048	\N	\N	\N	\N	\N	\N	OnTheMove Federated Conferences 2008 (DAO, COOP, GADA, ODBASE), Monterrey, Mexico	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	BraunSchmidtWalterZachariasODBASE08	http://publications.andreas.schmidt.name/BraunSchmidtWalterZachariasODBASE08_ontology_maturing.pdf	\N		\N	\N	\N	\N	\N	\N	timestamp = {2008.11.18}	Semantic technologies are very helpful in improving existing systems for searching, managing and retrieving of resources, e.g. image search, bookmarking or expert finder systems. They enhance these systems through background
\
knowledge stored in ontologies. However, in most cases, resources in these systems change very fast. In  consequence, they require a dynamic and agile
\
change of underlying ontologies. Also, the formality of these ontologies must fit the users needs and capabilities and must be appropriate and usable. Therefore, a continuous, collaborative and work or task integrated development of these ontologies is required. In this paper, we present how these requirements occur in real world applications and how they are solved and implemented using our Ontology Maturing Process Model.	4139d6e6bcc12e8b523c07e90bbee36e	9e48f6c32cf8fd61b55c3fadc986b22a	199d4ed905b55aba27f8f6c9c3d460fc	inproceedings	Using the Ontology Maturing Process Model for Searching, Managing and Retrieving Resources with Semantic Technologies	Simone Braun and Andreas Schmidt and Andreas Walter and Valentin Zacharias	\N	2008
7382620	D-Lib Magazine	11	\N	\N	April	\N	\N	\N	\N	{N}ature {P}ublishing {G}roup	\N	\N	\N	\N	hhls05social	http://www.dlib.org/dlib/april05/hammond/04hammond.html	\N		\N	\N	\N	\N	4	\N		\N	afcc2e296e0aef4559af11cde843813d	c7457d9dc07545a061de119d96ca4e47	89c6c43ad692ccfbe4c09d31926ab8a7	article	{S}ocial {B}ookmarking {T}ools ({I}): {A} {G}eneral {R}eview	Tony Hammond and Timo Hannay and Ben Lund and Joanna Scott	\N	2005
7385114	D-Lib Magazine	11	\N	\N	April	\N	\N	\N	\N	{N}ature {P}ublishing {G}roup	\N	\N	\N	\N	lhfh05social	http://www.dlib.org/dlib/april05/lund/04lund.html	\N		\N	\N	\N	\N	4	\N		\N	79632644447be4ea9da931633d427f56	46c0a98ab6ccb96ff4722f35781807de	13958ef5da2d2133b9b84e9a3cb40da1	article	{S}ocial {B}ookmarking {T}ools ({II}): {A} {C}ase {S}tudy - {C}onnotea	Ben Lund and Tony Hammond and Martin Flack and Timo Hannay	\N	2005
7386564	Artif. Intell.	118	\N	\N	\N	\N	\N	\N	\N	\N	Elsevier Science Publishers Ltd.	Essex, UK	\N	\N	350347CDF+00	\N	\N		\N	\N	69--113	\N	1-2	\N	issn = {0004-3702}	\N	d9ba79493c1a5801f0187b87732bad3c	68683ddac8974e9b3867c4b076a2b52f	edb61cca71b482e7eeb7f8956c1d9a4a	article	Learning to construct knowledge bases from the World Wide Web	Mark Craven and Dan DiPasquo and Dayne Freitag and Andrew McCallum and Tom Mitchell and Kamal Nigam and Se\\'{a}n Slattery	\N	2000
7389115	SIGKDD Explor. Newsl.	6	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Cimiano2004	http://portal.acm.org/citation.cfm?id=1046460	\N	Learning by googling	\N	\N	24--33	\N	2	\N	issn = {1931-0145}, doi = {http://doi.acm.org/10.1145/1046456.1046460}	The goal of giving a well-defined meaning to information is currently shared by endeavors such as the Semantic Web as well as by current trends within Knowledge Management. They all depend on the large-scale formalization of knowledge and on the availability of formal metadata about information resources. However, the question how to provide the necessary formal metadata in an effective and efficient way is still not solved to a satisfactory extent. Certainly, the most effective way to provide such metadata as well as formalized knowledge is to let humans encode them directly into the system, but this is neither efficient nor feasible. Furthermore, as current social studies show, individual knowledge is often less powerful than the collective knowledge of a certain community.As a potential way out of the knowledge acquisition bottleneck, we present a novel methodology that acquires collective knowledge from the World Wide Web using the GoogleTM API. In particular, we present PANKOW, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one with the aim of learning sub-/superconcept relations.	63ff0d46af1162d5f3f28c10b40e9315	b2ead36dfb325c7614f4149d69fde9c5	bd74f7a1354cb926b7d8cc96425d3584	article	Learning by googling	Philipp Cimiano and Steffen Staab	\N	2004
7392997	\N	5318	\N	\N	\N	\N	The Semantic Web - ISWC 2008	\N	\N	\N	Springer Berlin / Heidelberg	\N	\N	Lecture Notes in Computer Science	cattuto2008semantic	http://dx.doi.org/10.1007/978-3-540-88564-1_39	\N	SpringerLink - Buchkapitel	\N	\N	615--631	\N	\N	\N	isbn = {\	978-3-540-88563-4}, doi = {10.1007/978-3-540-88564-1_39}	Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks
\
like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.	90dbc4572b5d946ba760f0df7bac605a	b44538648cfd476d6c94e30bc6626c86	4752f261d03cead0c52565148a0ba1c9	inproceedings	Semantic Grounding of Tag Relatedness in Social Bookmarking Systems	Ciro Cattuto and Dominik Benz and Andreas Hotho and Gerd Stumme	\N	2008
7395581	J. Web Sem.	4	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	semwebminingjournal	\N	\N	DBLP Record 'journals/ws/StummeHB06'	\N	\N	124-143	\N	2	\N	ee = {http://dx.doi.org/10.1016/j.websem.2006.02.001}, bibsource = {DBLP, http://dblp.uni-trier.de}	\N	d4b32430089d99e85bdd227f8ce9bc42	3fd4efcf649ab35e8ef001f19b7ff83c	a038c36a8c161eba0bf46b3a6fa39dc1	article	Semantic Web Mining: State of the art and future directions	Gerd Stumme and Andreas Hotho and Bettina Berendt	\N	2006
7404385	A Stanford University Technical Report http://dbpubs. stanford. edu	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	haveliwala8090seg	\N	\N		\N	\N	\N	\N	\N	\N		\N	1ac554c549ef67030aa5cad3ccfc2aa7	0d40b575bd05c58b401f7a78ad3d4627	bc3bddcd6ea80eea5716492751bdff36	article	{The second eigenvalue of the Google matrix}	T.H. Haveliwala and S.D. Kamvar	\N	2003
7404526	\N	\N	\N	2nd	\N	\N	\N	\N	\N	\N	The MIT Press	\N	\N	\N	cormen01introduction	http://www.amazon.com/Introduction-Algorithms-Thomas-H-Cormen/dp/0262032937%3FSubscriptionId%3D13CT5CVB80YFWJEPWS02%26tag%3Dws%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0262032937	\N		\N	\N	\N	\N	\N	\N	ean = {9780262032933}, asin = {0262032937}, isbn = {0262032937}, dewey = {005.1}	\N	48a1ca26ba5768622be93dcdaa52cd7b	dcdeb0ec50a6798abf1724056982b543	225d42638921bee01b4ac8ff991b0a6a	book	Introduction to Algorithms	Thomas H. Cormen and Charles E. Leiserson and Ronald L. Rivest and Clifford Stein	\N	2001
7416629	New Media Society	6	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	SamLehman-Wilzig12012004	http://nms.sagepub.com/cgi/content/abstract/6/6/707	\N	The natural life cycle of new media evolution: Inter-media struggle for survival in the internet age -- Lehman-Wilzig and Cohen-Avigdor 6 (6): 707 -- New Media & Society	\N	\N	707-730	\N	6	\N	doi = {10.1177/146144804042524}, eprint = {http://nms.sagepub.com/cgi/reprint/6/6/707.pdf}	This article analyzes the evolution of the internet, with special emphasis on its impact on older media in their struggle to survive. The analysis is based on a   6-stage, natural life cycle model of new media evolution, comprising birth (technical invention), penetration, growth, maturity, self-defense, and adaptation,   convergence or obsolescence. Our universal model melds several elements of previous theories and analyses from disparate fields such as media history, marketing,   technological diffusion and convergence, while adding a few new aspects as well. The model's three contributions lie in expanding the scope   -quantitatively and qualitatively -of new media's  development stages (beyond the three or four stages noted by others); emphasizing the interaction and struggle between old and new media; and analyzing    functional-life after appliance-death'of media transformed/co-opted into something old/new. Applying this model to the internet enables us to better understand its future evolution and the survival chances of older mass media.
\
	6b85dd8e2c3be42699d1752fbca4b7e5	a8584049b3b775c9d11b7ad4e4a8f4a0	a02c5d593a5ac28457c6daedead616a5	article	{The natural life cycle of new media evolution: Inter-media struggle for survival in the internet age}	Sam Lehman-Wilzig and Nava Cohen-Avigdor	\N	2004
7416802	\N	\N	\N	1. Aufl.	\N	\N	\N	\N	\N	\N	Gabler Verlag	\N	\N	\N	3409117806	http://www.amazon.de/gp/redirect.html%3FASIN=3409117806%26tag=ws%26lcode=xm2%26cID=2025%26ccmID=165953%26location=/o/ASIN/3409117806%253FSubscriptionId=13CT5CVB80YFWJEPWS02	\N	Amazon.de: Crossmedia-Strategien. Dialog über alle Medien: Bernd Kracke: Bücher	\N	\N	\N	\N	\N	\N	ean = {9783409117807}, asin = {3409117806}, isbn = {3409117806}	\N	c957aa910dad4127b1621242ec4a70df	be7e6ad4a2431966a77a804e3411e370	10355efe7b7f7eddd3e9db2165b720ff	book	Crossmedia-Strategien. Dialog über alle Medien	Bernd Kracke	1. Auflage	2001
7416813	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Medienbruch	\N	\N		\N	\N	\N	\N	\N	\N		\N	665ae7f31d5d4c2902d01b8b0f578bf7	f4cdeb4ffd3e2ce838d000fb33391f35	0fae33f7e932921ee06f205e6718c158	book	Brücken über den Medienbruch: Crossmediale Strategien zeitgenössischer Printmedien	Stefan Schultz	LIT Verlag Berlin-Hamburg-Münster	2007
7416841	media perspektiven	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	16874365	http://www.media-perspektiven.de/261.html?&tx_mppublications_pi1[showUid]=1078&cHash=61cdcba724	\N		\N	\N	25-33	\N	\N	\N		\N	ab34c16095dcce64ebe87b343d0a10de	11b499f60f9c38e1c2c436f20ddec808	4d6e7023a38f8cef6a63119d0c1311c7	article	Von der Suchmaschine zum Werbekonzern Googles Ambitionen für ein crossmediales Werbenetzwerk	Ralf Kaumanns Veit Siegenheim	\N	2008
7416850	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Crossmediastrukturwandel	\N	\N	Vernetzte Kommunikation erfordert engere Zusammenarbeit zwischen Unternehmen, Agenturen und Medien.	\N	\N	\N	\N	18	\N		\N	799eb76646c199c622be2d927d066e97	cc88b80597947e50d22f9008533d4a51	94d885d5fd80d22e2475732806a3ab45	article	Crossmedia braucht Strukturwandel	Heidi Radvilas	Horizont	2004
7418938	\N	\N	\N	\N	\N	\N	\N	\N	World Wide Web Consortium	\N	\N	\N	\N	\N	rdf	http://www.w3.org/TR/1999/REC-rdf-syntax-19990222/	{W3C Recommendation}	My Main bibliography file	\N	\N	\N	\N	\N	\N		\N	9ac6743bc13a87ad3961cd133b0d59f0	0bde507d9f42a58c9e36f7fdfc65c7a0	2677b7b9a697a3005e906739f2882ae4	techreport	{Resource Description Framework (RDF) Model and Syntax Specification }	O. Lassila and R. R. Swick	\N	1999
7421670	Softw. Pract. Exper.	21	\N	\N	\N	\N	\N	\N	\N	\N	John Wiley \\& Sons, Inc.	New York, NY, USA	\N	\N	fr-layout	http://portal.acm.org/citation.cfm?id=137556.137557	\N		\N	\N	1129--1164	\N	11	\N	issn = {0038-0644}, doi = {http://dx.doi.org/10.1002/spe.4380211102}	\N	03ab42c4414032916ae551c910d0a2a4	db182bf8293d0e86786610b45e85d895	2f32e5eef767685d265d2e267b46240d	article	Graph drawing by force-directed placement	Thomas M. J. Fruchterman and Edward M. Reingold	\N	1991
7434478	\N	\N	\N	\N	\N	\N	Handbook of Quantitative Studies of Science and Technology	\N	\N	\N	\N	\N	\N	\N	Narin-Technology-1988	\N	\N		\N	\N	\N	\N	\N	\N		\N	4428fe137044433a30df59d67c68a615	d3b2c80b234a77a59313c9330b7a1eca	a5986a3c1ec92faa0992a1c0b2aec8ed	incollection	Technology indicators based on patents and patent citations.	Francis Narin and D. Olivastro	\N	1988
7434898	\N	\N	\N	\N	January	\N	\N	\N	W3C	\N	\N	\N	\N	\N	Kay07	\N	{W3C} Recommendation	XSL Transformations (XSLT) Version 2.0 - W3C Bibliography	\N	http://www.w3.org/TR/2007/REC-xslt20-20070123/	\N	\N	\N	\N	bibsource = {http://w2.syronex.com/jmr/w3c-biblio}	\N	855083bc534d579fff5f10a575b25ac3	9617f9b500a682da783e255b972366d4	4293a17f448e7af67021b56784c90082	techreport	{XSL} Transformations ({XSLT}) Version 2.0	Michael Kay	\N	2007
7434903	\N	\N	\N	\N	September	\N	\N	\N	W3C	\N	\N	\N	\N	\N	grddl	http://www.w3.org/TR/grddl/	W3C Recommendation		\N	\N	\N	\N	\N	\N		\N	3bbf7a65a05759fd467e81cd8a088742	6c68cddb539d0e33324658ce34afdc0a	3de02aac814f99daac92fe7e2a15db1b	techreport	Gleaning Resource Descriptions from Dialects of Languages {(GRDDL)}	W3C	Dan Connolly	2007
7445767	Journal of Computer-Mediated Communication	13	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	boyEll07	http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html	\N	Social Network Sites: Definition, History, and Scholarship	\N	http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html	\N	\N	1	\N		\N	2ae1f89f3e9708610a4808728a9a9e3d	d86d8d93ce52a5bb5ed01f11a609bd99	5fe9809ea6c3c9f43f96a7fc550f8e2a	article	Social network sites: Definition, history, and scholarship	Danah M. Boyd and Nicole B. Ellison	\N	2007
7445800	University of California Press	\N	\N	\N	\N	\N	Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability	\N	\N	\N	\N	\N	\N	\N	Mac67	http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html#macqueen	\N		\N	\N	281-297	\N	\N	\N	date = {(1967):}	\N	51c5b9ec2f1f429435d624ddcd1d5f46	8d7d4dfe7d3a06b8c9c3c2bb7aa91e28	55b2985db0b65d237559f6431dfded58	inproceedings	Some Methods for Classification and Analysis of Multivariate Observations	J. B. MacQueen	\N	1967
7445851	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	BookSurge Publishing	\N	\N	\N	DSNASSS2004a	\N	\N		\N	\N	\N	\N	\N	\N		\N	d75aebdf802b26f3171ef771a6cebcbe	b7fa882ef8fd11076cb393364ba42b33	87f661a6e903fbabb7619dc65fec46c8	book	The Development of Social Network Analysis: A Study in the Sociology of Science	Linton C. Freeman	\N	2004
7445860	Journal of Computer-Mediated Communication	13(1)	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Boyd2007	http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html	\N	Social Network Sites: Definition, History, and Scholarship	\N	http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html	\N	\N	\N	\N	date = {(2007).article 11.}	\N	cadf67cc7d495cf81db7df4cb06f68d9	d86d8d93ce52a5bb5ed01f11a609bd99	9741178b2d734704d85d3f891d486cb5	misc	Social network sites: Definition, history, and scholarship	Danah M. Boyd and Nicole B. Ellison	\N	2007
7445863	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cross2002	URL: http://www-1.ibm.com/services/us/imc/pdf/g510-1669-00-a-birds-eye-view-using-social-network-analysis.pdf	\N		\N	\N	\N	\N	\N	\N		\N	67668de5ab5f48ec337a9f703cc7ba84	731c432580d746cc07f053e2aca45c27	f8f4711ae7393d37a10e6b96b188e0ff	techreport	A bird's-eye view: Using social network analysis to improve knowledge creation and sharing	Rob Cross and Andrew Parker and Stephen P. Borgatti	\N	2002
7445876	\N	\N	\N	\N	\N	\N	WWW '01: Proceedings of the 10th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	SarKarKonRie01	http://portal.acm.org/citation.cfm?id=372071#	\N	Item-based collaborative filtering recommendation algorithms	\N	\N	285--295	\N	\N	\N	location = {Hong Kong, Hong Kong}, isbn = {1-58113-348-0}, doi = {http://doi.acm.org/10.1145/371920.372071}	\N	243acfe470284b5aba44008601ccf1c6	043d1aaba0f0b8c01d84edd517abedaf	b9b21995ad597c9329deb20fd11380d0	inproceedings	Item-based collaborative filtering recommendation algorithms	Badrul Sarwar and George Karypis and Joseph Konstan and John Riedl	\N	2001
7445880	\N	\N	\N	\N	October	\N	Proceedings of the Seventh International Conference on Music Information Retrieval (ISMIR'06)	\N	\N	\N	\N	Victoria, Canada	\N	\N	SPKW:ismir06:assigning	http://www.cp.jku.at/research/papers/Schedl_etal_ISMIR_2006.pdf	\N		\N	\N	\N	\N	\N	\N		\N	4922ef59a6c1dca2966e17970a303ee6	f5b10b340ddf947c21f9e7ac9ad9a0e9	ffa3b8d529ede65b8a4cde66e246810e	inproceedings	Assigning and Visualizing Music Genres by Web-based Co-Occurrence Analysis	Markus Schedl and Tim Pohle and Peter Knees and Gerhard Widmer	\N	2006
7446241	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cambridge University Press	\N	\N	\N	WasFau94	\N	\N		\N	\N	\N	\N	\N	\N		\N	770832a9405bfec99c8abb5f95dd1ace	387e48dafbb99962c628d30bfe9aa527	8c66c9e666572c9d11e6d8124c7ba567	book	Social Network Analysis: Methods and Applications	Stanley Wasserman and Katherine Faust	\N	1994
7454433	AI Communications	20	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cattuto2007	http://www.kde.cs.uni-kassel.de/hotho/pub/2007/aicomm_2007_folksonomy_clustering.pdf	\N		\N	\N	245 - 262	\N	4	\N	issn = {0921-7126}, vgwort = {67}	Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures – so-called folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag cooccurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior
\
such as spam.	d70721ce8ec7ffd8dcc8975c0a2c9fc8	fc5f2df61d28bc99b7e15029da125588	d87e198a6d564ae8a8fe151e0a96fa0f	article	Network Properties of Folksonomies	C. Cattuto and C. Schmitz and A. Baldassarri and V. D. P. Servedio and V. Loreto and A. Hotho and M. Grahl and G. Stumme	\N	2007
7458179	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	{Springer-Verlag}	Heidelberg	\N	\N	fensel_ontologies:silver_2003	\N	\N		\N	\N	\N	\N	\N	\N	isbn = {3540003029}	\N	e6ac9b3d34be45ce48e97a8659dd63bb	63d9bdba516e6115577e65f7386cf617	ebfad6978adcab409149abbc2e1f30f0	book	Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce	Dieter Fensel	\N	2003
7461253	First Monday	13	\N	\N	June	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cormode2008	http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2125/1972	\N		\N	\N	\N	\N	6	\N		\N	9ac09465ed1beaa62d416ed27796d15b	b5cf35c6862f7cf8ab85272a7acc7e13	f25d36fced3858467529805b44d9eb1b	article	Key differences between Web 1.0 and Web 2.0	Graham Cormode and Balachander Krishnamurthy	\N	2008
7465020	\N	\N	\N	\N	April	\N	\N	\N	Computer Science Department	\N	\N	\N	Standford University	\N	Heymann2006	http://dbpubs.stanford.edu:8090/pub/2006-10	\N		\N	\N	\N	\N	2006-10	\N	citeulike-article-id = {739394}, priority = {3}	Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.	b45e79bb97340492c01b37b6c1f6daaa	d77846b40aadb0e25233cabf905bb93e	f96e02d0c7f981abf76431a55533708b	techreport	Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems	P. Heymann and H. Garcia-Molina	\N	2006
7473844	Web Semantics: Science, Services and Agents on the World Wide Web	6	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	BuffSwee2008	http://dx.doi.org/10.1016/j.websem.2007.11.003	\N	ELIB Suchkatalog 2008	\N	\N	84-97	\N	1	\N	issn = {15708268}	Everyone agrees that user interactions and social networks are among the cornerstones of “Web 2.0”. Web 2.0 applications generally run in a web browser, propose dynamic content with rich user interfaces, offer means to easily add or edit content of the web site they belong to and present social network aspects.Well-known applications that have helped spread Web 2.0 are blogs, wikis, and image/video sharing sites; they have dramatically increased sharing and participation among web users. It is possible to build knowledge using tools that can help analyze users’ behavior behind the scenes: what they do, what they know, what they want. Tools that help share this knowledge across a network, and that can reason on that knowledge, will lead to users who can better use the knowledge available, i.e., to smarter users. Wikipedia, a wildly successful example of web technology, has helped knowledge-sharing between people by letting individuals freely create and modify its content. But Wikipedia is designed for people—today’s software cannot understand and reason on Wikipedia’s content. In parallel, the “semantic web”, a set of technologies that help knowledge-sharing across the web between different applications, is starting to gain attraction. Researchers have only recently started working on the concept of a “semantic wiki”, mixing the advantages of the wiki and the technologies of the semantic web. In this paper we will present a state-of-the-art of semantic wikis, and we will introduce SweetWiki, an example of an application reconciling two trends of the future web: a semantically augmented web and a web of social applications where every user is an active provider as well as a consumer of information. SweetWiki makes heavy use of semantic web concepts and languages, and demonstrates how the use of such paradigms can improve navigation, search, and usability.	99b1d15612a8882587d2b48df668bdd4	d5053388e2dd867670b437843c5e9713	cfd401d995a0cd3a12ba83deb593968f	article	SweetWiki: A semantic wiki	Michel Buffa and Fabien Gandon and Guillaume Ereteo and Peter Sander and Catherine Faron	\N	2008
7473925	Lecture Notes in Computer Science	1937	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	protege	\N	\N		\N	\N	69-82	\N	\N	\N		Knowledge-based systems have become ubiquitous in recent years. The World-Wide
\
Web consortium is developing the Resource Description Framework (RDF)—a system
\
for annotating even Web pages with knowledge elements. Knowledge-base developers
\
need to be able to share and reuse knowledge bases that they build. Therefore,
\
interoperability among different knowledge-representation systems is essential. The Open
\
Knowledge-Base Connectivity protocol (OKBC) is a common query and construction
\
interface for frame-based systems that facilitates this interoperability. Protégé-2000 is an
\
OKBC-compatible knowledge-base–editing environment developed in our laboratory.
\
Protégé-2000 has an easy-to-use and configurable interface. We describe its OKBCcompatible
\
knowledge model that makes the import and export of knowledge bases from
\
and to other knowledge-base servers easy. We discuss how the requirements of being
\
usable and configurable knowledge-acquisition tool affected our decisions in the
\
knowledge-model design. Protégé-2000 also has a flexible metaclass architecture which
\
provides configurable templates for new classes in the knowledge base. The use of
\
metaclasses makes Protégé-2000 easily extensible and enables its use with other
\
knowledge models. For example, we demonstrate that we can resolve many of the
\
differences between the knowledge models of Protégé-2000 and RDF by defining a new
\
metaclass set. Resolving the differences between the knowledge models in declarative
\
way enables easy adaptation of Protégé-2000 as an editor for other knowledgerepresentation
\
systems.	67e53453767239ab6ce805eddb0526fb	ffb04fc0225505e262adb8158ab38637	847b04330b213d4ffea900db6a15706a	article	The knowledge model of Protégé-2000: Combining interoperability and flexibility	N.F. Noy and R.W. Fergerson and M.A. Musen	\N	2000
7474433	Web Semantics	6	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	bojars08c	http://dx.doi.org/10.1016/j.websem.2007.11.010	\N		\N	\N	21-28	\N	1	\N	issn = {1570-8268}, timestamp = {2008.05.03}, file = {ScienceDirect:2008/BojarBreslinEtAl08jws.pdf:PDF}, owner = {flint}	Large volumes of content (bookmarks, reviews, videos, etc.) are currently
\
\	being created on the Social Web, i.e. on Web 2.0 community sites,
\
\	and this content is being annotated and commented upon. The ability
\
\	to view an individual's entire contribution to the Social Web would
\
\	be an interesting and valuable service, particularly important as
\
\	social networks are often being formed through created content and
\
\	things that people have in common (object-centred sociality). SIOC
\
\	is a Semantic Web research project that aims to describe online communities
\
\	on the Social Web. This paper describes how SIOC and the Semantic
\
\	Web can enable linking and reuse scenarios of data from Web 2.0 community
\
\	sites, and introduces a SIOC Types module to further specify the
\
\	type of content items and act as a ´glue¡ between user posts and
\
\	the content items created and annotated by users.	97a56abff5f63d305d8ffb17de2ece34	4e5f28a831febf830760f62b52241669	cd57c91799de60c437e8cf61282efd7a	article	Using the Semantic Web for Linking and Reusing Data across Web 2.0 Communities	Uldis Bojars and John G. Breslin and Aidan Finn and Stefan Decker	\N	2008
7482130	\N	\N	\N	\N	\N	\N	Dictionary of Bibliometrics	\N	\N	\N	Haworth Press, Inc.	10 Alice St., Binghamton, NY 13904-1580 	\N	\N	Virgil(1994)	http://eric.ed.gov/ERICWebPortal/custom/portlets/recordDetails/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED386214&ERICExtSearch_SearchType_0=no&accno=ED386214	\N	Literatur for Information	\N	ISBN-1-56024-852-1	\N	\N	\N	\N		This dictionary explains 225 terms used in bibliometrics, and it provides nontechnical definitions of bibliometric concepts and suggests sources where more information can be found about the defined term. Special features include sample references, cross references, variants (synonyms), and an index of names. The introduction relates the terms of bibliometrics, informetrics, and scientometrics; describes the areas of scholarly communication and evaluation of information services; and explains mathematical terminology and symbols. (AEF)	9a636f8baa50df61de626a6bf799e9ed	11f65646f3673d9e5c20f9d4dd6615be	492e35f9d90623be59f7358a4195ddf5	book	Dictionary of Bibliometrics	Virgil P. Diodato	\N	1994
7511948	\N	\N	\N	\N	\N	\N	SIAM Data Mining	\N	\N	\N	\N	\N	\N	\N	white2005sca	\N	\N		\N	\N	\N	\N	\N	\N		\N	1eae1beb663173f74dd38434cc343385	180d37026ab6ea4f4c3f6aba9c405929	310763d5fe7195d89883c91c90681e03	article	{A spectral clustering approach to finding communities in graph}	S. White and P. Smyth	\N	2005
7511960	Arxiv preprint cond-mat/0603718	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	reichardt2006smc	\N	\N		\N	\N	\N	\N	\N	\N		\N	75f7420f7cf73f3f347fe248e65fe75d	968ba40de011d4560e3d1279bee169ac	787390b28f103b690a59e0949898d93d	article	{Statistical mechanics of community detection}	J. Reichardt and S. Bornholdt	\N	2006
7511985	Science	286	\N	\N	October	\N	\N	\N	\N	\N	\N	Department of Physics, University of Notre Dame, Notre Dame, IN 46556, USA.	\N	\N	citeulike:90557	http://view.ncbi.nlm.nih.gov/pubmed/10521342	\N	Statistical properties of random networks	\N	\N	509--512	\N	5439	\N	posted-at = {2006-02-08 01:49:16}, issn = {0036-8075}, citeulike-article-id = {90557}, priority = {2}	Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.	92ab44b249c353fd01825272c22d04f3	89d3f086051d18093558698788063dfe	8e36ff86b30d57b43045d6ad0de2c0a5	article	Emergence of scaling in random networks	A. L. Barabasi and R. Albert	\N	1999
7512087	Proceedings of the National Academy of Sciences	103	\N	\N	\N	\N	\N	\N	\N	\N	National Acad Sciences	\N	\N	\N	newman2006mac	\N	\N		\N	\N	8577--8582	\N	23	\N		\N	90113c14405f0b5991d9719fdc23649f	e664336d414a1e21d89f30cc56f5e739	9104cb1678a39c96b06ed838a8aa3a63	article	{Modularity and community structure in networks}	MEJ Newman	\N	2006
7513211	\N	3244	\N	\N	\N	\N	ALT	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	conf/alt/HutterP04	http://dblp.uni-trier.de/db/conf/alt/alt2004.html#HutterP04	\N	dblp	\N	\N	279-293	\N	\N	conf/alt/2004	date = {2004-10-05}, ee = {http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3244&spage=279}, isbn = {3-540-23356-3}	\N	d7da60ea0573f5e8fffd8082056b0d24	e358f6a61c2ed3874d8a8af1e5a50f91	bdf4fc032ab976dce362febcf34a3231	inproceedings	Prediction with Expert Advice by Following the Perturbed Leader for General Weights.	Marcus Hutter and Jan Poland	Shai Ben-David and John Case and Akira Maruoka	2004
7513638	\N	5318	\N	\N	\N	\N	The Semantic Web - ISWC 2008, Proc.Intl. Semantic Web Conference 2008	\N	\N	\N	Springer	Heidelberg	\N	LNAI	cattuto2008semantic	http://dx.doi.org/10.1007/978-3-540-88564-1_39	\N		\N	\N	615--631	\N	\N	\N		Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks
\
like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.	81b8a42c8600cadaf289d5f91fae41b6	b44538648cfd476d6c94e30bc6626c86	25f7c308c1fc943215af306e76e458b0	inproceedings	Semantic Grounding of Tag Relatedness in Social Bookmarking Systems	Ciro Cattuto and Dominik Benz and Andreas Hotho and Gerd Stumme	Amit P. Sheth and and Steffen Staab and and Mike Dean and and Massimo Paolucci and and Diana Maynard and and Timothy W. Finin and and Krishnaprasad Thirunarayan	2008
7515354	SIAM Review	45	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	New03	\N	\N		\N	\N	167-256	\N	2	\N		\N	b0b1d0fa1346c9bcf272210835914e90	7bedd01cb4c06af9f5200b0fb3faa571	f0de28071b8ee1c3675e67c7538e806a	article	The structure and function of complex networks	M. E. J. Newman	\N	2003
7530812	Proceedings of the International Conference on Software Maintenance	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Caprile2000	\N	\N		\N	\N	97-107	\N	\N	\N	issn = {}, doi = {10.1109/ICSM.2000.883022}	The identifiers chosen by programmers as entity names contain valuable information. They are often the starting point for the program understanding activities, especially when high level views, like the call graph, are available. An approach for the restructuring of program identifier names is proposed, aimed at improving their meaningfulness. It considers two forms of standardization, associated respectively to the lexicon of the composing terms and to the syntax of their arrangement. Automatic and semiautomatic techniques are described which can help the restructuring intervention. Their application to a real world case study is also presented	a136a4e0acd0eb4e6a65f2c6b4d08583	57ba06d5a70ea37da27aad7b56575ef3	2b67fc5cec16ad84e325e90aca4820e8	inproceedings	Restructuring program identifier names	Bruno Caprile and Paolo Tonella	\N	2000
7533202	SIGKDD Explor. Newsl.	6	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Cimiano04	http://portal.acm.org/citation.cfm?id=1046460	\N	Learning by googling	\N	\N	24--33	\N	2	\N	issn = {1931-0145}, doi = {http://doi.acm.org/10.1145/1046456.1046460}	The goal of giving a well-defined meaning to information is currently shared by endeavors such as the Semantic Web as well as by current trends within Knowledge Management. They all depend on the large-scale formalization of knowledge and on the availability of formal metadata about information resources. However, the question how to provide the necessary formal metadata in an effective and efficient way is still not solved to a satisfactory extent. Certainly, the most effective way to provide such metadata as well as formalized knowledge is to let humans encode them directly into the system, but this is neither efficient nor feasible. Furthermore, as current social studies show, individual knowledge is often less powerful than the collective knowledge of a certain community.As a potential way out of the knowledge acquisition bottleneck, we present a novel methodology that acquires collective knowledge from the World Wide Web using the GoogleTM API. In particular, we present PANKOW, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one with the aim of learning sub-/superconcept relations.	63ff0d46af1162d5f3f28c10b40e9315	b2ead36dfb325c7614f4149d69fde9c5	bd74f7a1354cb926b7d8cc96425d3584	article	Learning by googling	Philipp Cimiano and Steffen Staab	\N	2004
7533274	\N	\N	\N	\N	\N	\N	WWW '07: Proceedings of the 16th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Chirita07	http://portal.acm.org/citation.cfm?id=1242686	\N	P-TAG	\N	\N	845--854	\N	\N	\N	location = {Banff, Alberta, Canada}, isbn = {978-1-59593-654-7}, doi = {http://doi.acm.org/10.1145/1242572.1242686}	\N	78db7a285d9a523728cf88fd83c5190c	93612f3a17257a5d0d73e95d59d0c408	58f1705eb270c20c6aaea2377f88e664	inproceedings	P-TAG: large scale automatic generation of personalized annotation tags for the web	Paul Alexandru Chirita and Stefania Costache and Wolfgang Nejdl and Siegfried Handschuh	\N	2007
7538860	PNAS	99	\N	\N	June	\N	\N	\N	\N	\N	\N	\N	\N	\N	GirNew02	\N	\N		\N	\N	7821-7826	\N	12	\N		\N	8179bc1e9f9fc4c29421d292076ef0a7	ecd7a48a37f660ab421472140168c892	ec20851eb4909dd27cefec2dc9883fa4	article	Community structure in social and biological networks	M. Girvan and M. E. J. Newman	\N	2002
7544641	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Scerri2008semanticemail	\N	\N		\N	\N	\N	\N	\N	\N		In this paper we provide a summary of work that has been
\
pursued in the area of Semantic Email, with a particular
\
focus on our work in the area. The aim of this paper is to
\
provide a status quo for this topic, as well as to generate
\
ideas and discussions that could evolve the topic and take it
\
to new heights. We finish off by outlining future directions
\
for evaluation, improvement as well as extension of our
\
current technologies.	c8ffa671c10aacd05a0ff25d06529751	1be28487c5478a5b8e34ed4905365147	a815716e0ed34426ca5e3a62b89d5d6c	inproceedings	The path towards Semantic Email: Summary and Outlook	Simon Scerri Brian Davis Siegfried Handschuh	\N	2008
7544798	Journal of the American society for information science	41	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	deerwester1990ils	\N	\N		\N	\N	391--407	\N	6	\N		\N	761c631f8beca41e2e1445eb3b07a5e5	c15e0f019b2b967d224e7443100e8ff9	c4b3c80072a4c342ac64c663401db5cb	article	{Indexing by latent semantic analysis}	S. Deerwester and S.T. Dumais and G.W. Furnas and T.K. Landauer and R. Harshman	\N	1990
7641991	SIGKDD Explorations	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	gaertner2003	A Survey of Kernels for Structured Data	\N		\N	\N	\N	\N	\N	\N		\N	6b0b51caaea332f89ae61ab581fce0cf	50a4a3a44163e7dc7c8af374bbe26b78	9228c8db7052bd327f57e616083de583	article	A Survey of Kernels for Structured Data	Thomas Gärtner	\N	2003
7642162	\N	\N	\N	\N	\N	\N	CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	1180903	http://portal.acm.org/citation.cfm?id=1180903	\N	Don't look stupid	\N	\N	171--180	\N	\N	\N	location = {Banff, Alberta, Canada}, isbn = {1-59593-249-6}, doi = {http://doi.acm.org/10.1145/1180875.1180903}	If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.	e0bec3c40467ecec1f5dc35688b84672	24be686d042a3a4a710d9ff22dee0f2e	9cff83fa18b0be12f637cd3c1bb952be	inproceedings	Don't look stupid: avoiding pitfalls when recommending research papers	Sean M. McNee and Nishikant Kapoor and Joseph A. Konstan	\N	2006
7642472	\N	\N	\N	\N	\N	\N	ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning	\N	\N	\N	Morgan Kaufmann Publishers Inc.	San Francisco, CA, USA	\N	\N	kashima2002	http://www.comp.nus.edu.sg/~atung/renmin/icml2002.pdf	\N		\N	\N	291--298	\N	\N	\N		\N	3c0ec65a0a5b58aa36ffc769bf146569	a4652cfc566d22473bf759726f53994f	d4bc6c9fbead2a0ff87b2f24584130d4	inproceedings	Kernels for Semi-Structured Data	Hisashi Kashima and Teruo Koyanagi	\N	2002
7645121	Erfolgreiches Management von Bibliotheken und Informationseinrichtungen	\N	\N	\N	\N	\N	\N	\N	\N	\N	Verlag Dash\\"ofer	\N	\N	\N	eprints313808	http://eprints.rclis.org/13808/	\N	Bibliothek 2.0 - Perspektiven, Probleme, Handlungsbereiche	\N	\N	1--30	\N	\N	\N		Assumptions on the development prospects of library 2.0 are stated. According to these assumptions, the professionals working at the library are key components of a library 2.0. Following up to this thesis, principles and key problems of enhancing "librarians' 2.0" skills are discussed. The chapter concludes with an outline of some important and typical building blocks of a library 2.0. These are a) new ways to engage with users, b) bringing library services and data into the users' information space, c) enhancing the focus of
\
libraries' collection and classification activities with freely availabe informations on the web. All references and examples are reachable from the author's account at the social bookmarking service delicious under the tag embi08, cf. http://delicious.com/lambo/embi08.
\

\
	6656a5a2fa6d1c1980627b1a36e30925	432f978368f57fe7abb6c08f1de9fe0b	31cb649772dd0c35c564383cee73496c	misc	Bibliothek 2.0 - Perspektiven, Probleme, Handlungsbereiche	Lambert Heller	Konrad Umlauf and Hans-Christoph Hobohm	2008
7652560	\N	\N	\N	\N	Oct	\N	Proceedings of the International Conference on Software Maintenance. 	\N	\N	\N	\N	\N	\N	\N	Pearse1995	\N	\N		\N	\N	295-303	\N	\N	\N	issn = {}, doi = {10.1109/ICSM.1995.526551}	It is clear that the burden of software maintenance increases proportionately with our inventory of software systems. Our inventory is increasing because we now recognize existing code as reusable assets rather than liabilities. With this recognition comes an understanding of the importance of evaluating code quality and maintainability. We show how maintainability metrics can be used to gauge the effect of perfective and adaptive maintenance on large industrial software systems. In a series of four studies we measure the maintainability of the source code before and after a prescribed maintenance activity. These measurements permit us to analyze the effect of the maintenance activities as well as evaluating the model's sensitivity to code change 	84d16e964620614667cbb28c2f063ace	010dca8e4e051bfaffb114a3f7fe76cd	df5b5d7b7fea83095dba447a3eb33cfe	inproceedings	Maintainability measurements on industrial source code maintenance activities	T. Pearse and P. Oman	\N	1995
7652561	ACM Comput. Surv.	31	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	331504	http://portal.acm.org/citation.cfm?id=331499.331504&coll=Portal&dl=ACM&CFID=26215063&CFTOKEN=18848029	\N	Data clustering	\N	\N	264--323	\N	3	\N	issn = {0360-0300}, doi = {http://doi.acm.org/10.1145/331499.331504}	Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.	3f68466c95d0d087243df2cff7c15de5	5113b61d428d4de4423182e5f2b2f468	b19bcef82a04eb82ee4abde53ee7d1c2	article	Data clustering: a review	A. K. Jain and M. N. Murty and P. J. Flynn	\N	1999
7658861	\N	\N	\N	\N	\N	\N	Proceedings of the 4th Workshop on Scripting for the Semantic Web	\N	\N	\N	\N	\N	\N	\N	PHBB08	http://CEUR-WS.org/Vol-368/paper11.pdf	\N	Paper describing a semantic microblogging solution.	\N	\N	\N	\N	\N	CEUR-WS.org/Vol-368	timestamp = {2008.07.08}	\N	fa748861a5edfd25ece61d75c99a2dab	53059f123b75dc5974bbff9b39eecfe1	4b2d0e2620a0555de648281b3c6737de	inproceedings	Microblogging: A Semantic Web and Distributed Approach	Alexandre Passant and Tuukka Hastrup and Uldis Bojars and John Breslin	\N	2008
7661168	Communications of the Association for Information Systems	13	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Wagner2004	http://cais.isworld.org/articles/13-19/default.asp?View=Journal&x=52&y=11	\N		\N	\N	265-289	\N	\N	\N		\N	9db4c84ab318cfea0030e73c18c98c96	7ff0ac5f6cfed70568ad293e98ea1e35	6c61ad4fe3384969651178c708e6fe9c	article	Wiki: A Technology for Conversational Knowledge Management and Group Collaboration.	Christian Wagner	\N	2004
7663889	\N	\N	\N	\N	\N	\N	WWW '07: Proceedings of the 16th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Bao07	http://portal.acm.org/citation.cfm?id=1242640	\N	Optimizing web search using social annotations	\N	\N	501--510	\N	\N	\N	location = {Banff, Alberta, Canada}, isbn = {978-1-59593-654-7}, doi = {http://doi.acm.org/10.1145/1242572.1242640}	\N	6a9d7f692cb34848565301082abbe4af	2cbdc7da88c90ef22468108c1f481159	b9966b9df0199a0b7b2d5a1b0d7560cb	inproceedings	Optimizing web search using social annotations	Shenghua Bao and Guirong Xue and Xiaoyuan Wu and Yong Yu and Ben Fei and Zhong Su	\N	2007
7664088	\N	\N	\N	\N	\N	\N	Agent-Mediated Knowledge Management	\N	\N	\N	Springer Berlin / Heidelberg	\N	\N	\N	elst2003	http://www.springerlink.com/content/7w83u24a12wv5fc0	\N		\N	\N	1--30	\N	\N	\N		In this paper, we outline the relation between Knowledge Management (KM) as an application area on the one hand, and software agents as a basic technology for supporting KM on the other. We start by presenting characteristics of KM which account for some drawbacks of today’s – typically centralized – technological approaches for KM. We argue that the basic features of agents (social ability, autonomy, re- and proactiveness) can alleviate several of these drawbacks. A classification schema for the description of agent-based KM systems is established, and a couple of example systems are depicted in terms of this schema. The paper concludes with questions which we think research in Agentmediated Knowledge Management (AMKM) should deal with.	156d927c58e7cecdf72bf68e8c06a071	16e13c7a472220ddf2c4aae6fd10799f	96e35918d3ebd9c28e155e1da2ac968f	inbook	Towards Agent-Mediated Knowledge Management	L. van Elst and V. Dignum and A. Abecker	\N	2003
7666592	\N	\N	\N	\N	\N	\N	CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management	\N	\N	\N	ACM	New York, NY, USA	\N	\N	956972	http://portal.acm.org/citation.cfm?doid=956863.956972#IndexTerms	\N	The link prediction problem for social networks	\N	\N	556--559	\N	\N	\N	location = {New Orleans, LA, USA}, isbn = {1-58113-723-0}, doi = {http://doi.acm.org/10.1145/956863.956972}	Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.	0ace17090389786252e998992dff3fa3	1920da9fec5905f031a0ab3919553362	5ea4aec308221402c617efa9559e5ee5	inproceedings	The link prediction problem for social networks	David Liben-Nowell and Jon Kleinberg	\N	2003
7667857	\N	\N	\N	\N	\N	\N	CSCW '08: Proceedings of the ACM 2008 conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	1460641	http://portal.acm.org/citation.cfm?id=1460641	\N		\N	\N	485--494	\N	\N	\N	location = {San Diego, CA, USA}, isbn = {978-1-60558-007-4}, doi = {http://doi.acm.org/10.1145/1460563.1460641}	\N	ad2cb5cd0546f8cc84dce47937c47ff2	8ee7d17052c9334c24a2a6a6c829fed0	3fc03928d68ed6b71000db8fa0bf6010	inproceedings	Towards a model of understanding social search	Brynn M. Evans and Ed H. Chi	\N	2008
7735637	EDUCAUSE Review	43 	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	AlexanderLevine08	http://connect.educause.edu/Library/EDUCAUSE+Review/Web20StorytellingEmergenc/47444?time=1225663590	\N	BibSonomy::bibtex::Web 2.0 Storytelling: Emergence of a New Genre	\N	\N	\N	\N	6	\N		A story has a beginning, a middle, and a cleanly wrapped-up ending. Whether told around a campfire, read from a book, or played on a DVD, a story goes from point A to B and then C. It follows a trajectory, a Freytag Pyramid—perhaps the line of a human life or the stages of the hero's journey. A story is told by one person or by a creative team to an audience that is usually quiet, even receptive. Or at least that’s what a story used to be, and that’s how a story used to be told. Today, with digital networks and social media, this pattern is changing. Stories now are open-ended, branching, hyperlinked, cross-media, participatory, exploratory, and unpredictable. And they are told in new ways: Web 2.0 storytelling picks up these new types of stories and runs with them, accelerating the pace of creation and participation while revealing new directions for narratives to flow.	6f3851342a86947a8397c907e4170d4c	e0aab7ecd77ea44dc4547979d3e82f08	df5f14ad8117c27da82d3906b3205c65	article	Web 2.0 Storytelling: Emergence of a New Genre	Bryan Alexander and Alan Levine	\N	2008
7735648	\N	\N	\N	\N	\N	\N	Intelligent Narrative Technologies: Papers from the AAAI Fall Symposium	\N	\N	\N	FS-AAAI Press	\N	\N	\N	mueller2007	\N	\N	Erik T. Mueller	\N	\N	 95-101	\N	FS-07-05	\N	location = {Menlo Park, CA:}, date = {(2007)}	\N	03b42624ed768e864d970fe945e6a2ee	f3e9d51ba5597b9a1da25aea1a9e576a	bec1a93e7330950a62cb126e0cb4b785	techreport	Understanding goal-based stories through model finding and planning	E.T. Mueller	B.S. Magerko & M. O. Riedl	2007
7755334	\N	\N	\N	\N	Jun.	\N	Community Computing and Support Systems	\N	\N	\N	Springer	Berlin	\N	\N	Schlichter1998	\N	\N		\N	\N	77-93	\N	\N	\N	entrytype = {incollection}	\N	08ddd9b0ca87e40fb443293bfbfcc2be	bb8668f0b5e98ce33897a87805e84f5c	380edf04543ed6cb0aceea6342c7bc96	incollection	Awareness - The Common Link Between Groupware and Community Support System	Johann Schlichter and Michael Koch and Changmao Xu	T. Ishida	1998
7755390	ACM Trans. Comput.-Hum. Interact.	14	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	MMS+08	http://doi.acm.org/10.1145/1314683.1314684	\N		\N	\N	\N	\N	4	\N		\N	a6e3da2e0b9244cdac98a584635e0502	7751cb8a12fb003928a8524620ed2176	a3649c6ebfbb0250c3b8def036ca11dc	article	Proactive Displays: Supporting Awareness in Fluid Social Environments	David W. McDonald and Joseph F. McCarthy and Suzanne Soroczak and David H. Nguyen and Al M. Rashid	\N	2008
7764392	\N	\N	\N	\N	\N	\N	In Proceedings of the Workshop on Mobile Computing Systems and Applications	\N	\N	\N	IEEE Computer Society	\N	\N	\N	Schilit94context-awarecomputing	\N	\N	This paper describes software that examines and reacts to an individual’s changing context. Such software can promote and mediate people’s interactions with devices, computers, and other people, and it can help navigate unfamiliar places. We believe that a limited amount of information covering a person’s proximate environment is most important for this form of computing since the interesting part of the world around us is what we can see, hear, and touch. In this paper we define context-aware computing, and describe four categories of context-aware applications: proximate selection, automatic contextual reconfiguration, contextual information and commands, and context-triggered actions. Instances of these application types have been prototyped on the PARCTAB, a wireless, palm-sized computer.	\N	\N	85--90	\N	\N	\N		This paper describes software that examines and reacts to an individual’s changing context. Such software can promote and mediate people’s interactions with devices, computers, and other people, and it can help navigate unfamiliar places. We believe that a limited amount of information covering a person’s proximate environment is most important for this form of computing since the interesting part of the world around us is what we can see, hear, and touch. In this paper we define context-aware computing, and describe four categories of context-aware applications: proximate selection, automatic contextual reconfiguration, contextual information and commands, and context-triggered actions. Instances of these application types have been prototyped on the PARCTAB, a wireless, palm-sized computer.	178ee15c2d4f91677d2137ce87cc717e	3c02c4f97d93bb8ab01b81862e57b58f	3a423a4506a4d15ab8ba66cc156812ca	inproceedings	Context-aware computing applications	Bill N. Schilit and Norman Adams and Roy Want	\N	1994
7771325	Progress in Artificial Intelligence	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	keyhere	http://dx.doi.org/10.1007/11595014_22	\N	SpringerLink - Book Chapter	\N	\N	218--231	\N	\N	\N		Representing knowledge about researchers and research communities is a prime use case for distributed, locally maintained, interlinked and highly structured information in the spirit of the Semantic Web. In this paper we describe the publicly available ‘Semantic Web for Research Communities’ (	4d24e7b08e2fac612b5ec43812f7c02a	2bc7b1b25e3ed3fd4edc0749888dd3e1	817dae00248c81be82ff0a3955886e51	article	The SWRC Ontology - Semantic Web for Research Communities.	York Sure and Stephan Bloehdorn and Peter Haase and Jens Hartmann and Daniel Oberle	\N	2005
7775459	\N	\N	\N	\N	\N	\N	Workshop on The What, Who, Where, When, and How of Context-Awareness	\N	\N	\N	\N	\N	\N	2000 Conference on Human Factors in Computing Systems	contextII	\N	\N		\N	\N	\N	\N	\N	\N		\N	e2d1d6621331b1cd1a9ee175974706d3	4bb1b5b9aceb4c72f73c8d533835a8ff	2bddbaa68ea17b1829d6af74cb92e154	inproceedings	Towards a Better Understanding of Context and Context-Awareness	Anind K. Dey and Gregory D. Abowd	\N	2000
7776576	\N	\N	\N	\N	June	\N	The Semantic Web: Research and Applications	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	hoser2006semantic	\N	\N		\N	Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro	\N	\N	\N	\N		A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very
\
 few SNA applications, and virtually none for analyzing the
\
 structure of ontologies.
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We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.	00cb45dd4a5258f7b9d152018ce8f26d	344ec3b4ee8af1a2c6b86efc14917fa9	9a2c77c7c7a1b19cd16df08cca65f706	inproceedings	Semantic Network Analysis of Ontologies	Bettina Hoser and Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme	\N	2006
7776659	\N	\N	\N	\N	\N	\N	Data Science and Classification: Proc. of the 10th IFCS Conf.	\N	\N	\N	Springer	Berlin, Heidelberg	\N	Studies in Classification, Data Analysis, and Knowledge Organization	schmitz2006mining	\N	\N		\N	\N	261--270	\N	\N	\N		\N	d483d17c76f0c6773b02d96162678962	20650d852ca3b82523fcd8b63e7c12d7	1e79a0f1c79561073d14434adce1e890	inproceedings	Mining Association Rules in Folksonomies	Christoph Schmitz and Andreas Hotho and Robert Jäschke and Gerd Stumme	V. Batagelj and H.-H. Bock and A. Ferligoj and A. {\\v Z}iberna	2006
7795858	Machine Learning	20	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	cortes&vapnik1995	http://lyle.smu.edu/~mhd/8331f05/svm.pdf	\N		\N	\N	273-297	\N	\N	\N		\N	c954702898fb98dc426d61c1a4ab0b48	c223c465141618ad63aac5a6132280f7	ad7006d3b1b3b3b1882633d98f52d410	article	Support--Vector Networks	Corinna Cortes and Vladimir Vapnik	\N	1995
7795928	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Springer	New York	\N	\N	vapnik1999	http://books.google.com/books?id=sna9BaxVbj8C&printsec=frontcover	\N		\N	\N	\N	\N	\N	\N		\N	e0c341af55709905d4535f4938e3cf58	98898576dfede7a15fcbf60cad98f9c0	2f74bbccd3c0defd183733b6c27529e9	book	The Nature of Statistical Learning Theory	Vladimir Vapnik	\N	1995
7803895	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cambridge University Press	\N	\N	\N	manning2008	http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html	\N		\N	\N	\N	\N	\N	\N		\N	ccb78ee861a0349fae2f505bb8976017	2e574e46b7668a7268e7f02b46f4d9bb	2588419fae77ef64bd735f4265f7daa5	book	Introduction to Information Retrieval	C. D. Manning and P. Raghavan and H. Schütze	\N	2008
7805605	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cambridge University Press	\N	\N	\N	manning2008	http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html	\N		\N	\N	\N	\N	\N	\N		\N	ccb78ee861a0349fae2f505bb8976017	2e574e46b7668a7268e7f02b46f4d9bb	2588419fae77ef64bd735f4265f7daa5	book	Introduction to Information Retrieval	C. D. Manning and P. Raghavan and H. Schütze	\N	2008
7806276	\N	\N	\N	2	\N	\N	\N	\N	\N	\N	Butterworths	London	\N	\N	vanRijsbergen1979	\N	\N		\N	\N	\N	\N	\N	\N		\N	d3ea5eda784a09442c8616086323ca7c	0edccdac9af024f458911b82f61686ab	b53893655b48140d4310a848dbf204d3	book	Information retrieval	C. J. van Rijsbergen	\N	1979
7808108	Web Semantics: Science, Services and Agents on the World Wide Web	4	\N	\N	June	\N	Semantic Grid --The Convergence of Technologies	\N	\N	\N	\N	\N	\N	\N	citeulike:754858	http://dx.doi.org/10.1016/j.websem.2006.02.001	\N		\N	\N	124--143	\N	2	\N	posted-at = {2008-01-23 08:37:03}, citeulike-article-id = {754858}, priority = {2}, doi = {http://dx.doi.org/10.1016/j.websem.2006.02.001}	Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today's Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.	c56b6cc0179968cb6e3942ac818b83be	3fd4efcf649ab35e8ef001f19b7ff83c	2268f71e048f1e2d6414db7768331cbd	article	Semantic Web Mining:  State of the art and future directions	Gerd Stumme and Andreas Hotho and Bettina Berendt	\N	2006
7808111	The Semantic Web: Research and Applications	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	citeulike:1450024	http://dx.doi.org/10.1007/978-3-540-72667-8\\_44	\N		\N	\N	624--639	\N	\N	\N	posted-at = {2008-01-23 07:42:16}, priority = {2}, citeulike-article-id = {1450024}, doi = {http://dx.doi.org/10.1007/978-3-540-72667-8\\_44}	While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.	481d3f757fcf8e4d28b642b670d91f59	b828fbd5c9ddc4f9551f973445ecb283	f9d5bb1188a441230ccabdb2a02eb267	incollection	Integrating Folksonomies with the Semantic Web	Lucia Specia and Enrico Motta	\N	2007
7809895	\N	\N	\N	\N	\N	\N	CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management	\N	\N	\N	ACM	New York, NY, USA	\N	\N	956972	http://doi.acm.org/10.1145/956863.956972	\N	The link prediction problem for social networks	\N	\N	556--559	\N	\N	\N	location = {New Orleans, LA, USA}, isbn = {1-58113-723-0}, doi = {http://doi.acm.org/10.1145/956863.956972}	Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.	0ace17090389786252e998992dff3fa3	1920da9fec5905f031a0ab3919553362	5ea4aec308221402c617efa9559e5ee5	inproceedings	The link prediction problem for social networks	David Liben-Nowell and Jon Kleinberg	\N	2003
7810233	\N	\N	\N	\N	\N	\N	Bridging the Gep between Semantic Web and Web 2.0 (SemNet 2007)	\N	\N	\N	\N	\N	\N	\N	Angeletou:2007	http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/BridgingtheGap.pdf	\N		\N	\N	30-43	\N	\N	\N		While folksonomies allow tagging of similar resources with
\
a variety of tags, their content retrieval mechanisms are severely hampered
\
by being agnostic to the relations that exist between these tags.
\
To overcome this limitation, several methods have been proposed to find
\
groups of implicitly inter-related tags. We believe that content retrieval
\
can be further improved by making the relations between tags explicit. In
\
this paper we propose the semantic enrichment of folksonomy tags with
\
explicit relations by harvesting the Semantic Web, i.e., dynamically selecting
\
and combining relevant bits of knowledge from online ontologies.
\
Our experimental results show that, while semantic enrichment needs to
\
be aware of the particular characteristics of folksonomies and the Semantic
\
Web, it is beneficial for both.	5db2ea573ce93a0a7daa69ce03a85a23	2c39b32de140050ffaec3ff6df4ce395	32ffa336026f918623358698ffb1c578	inproceedings	Bridging the Gap Between Folksonomies and the
\
Semantic Web: An Experience Report	Sofia Angeletou and Marta Sabou and Lucia Specia and Enrico Motta	\N	2007
7810264	\N	\N	\N	\N	March	\N	Proceedings of the First International Conference on Weblogs and Social Media (ICWSM)	\N	\N	\N	\N	Boulder, Colorado	\N	\N	passant:uos	http://www.icwsm.org/papers/paper15.html	\N		\N	\N	\N	\N	\N	\N		While free-tagging classification is widely used in social software implementations and especially in weblogs, it raises various issues regarding information retrieval. In this paper, we describe an approach that mixes folksonomies and semantic web technologies in order to solve some of these problems, and to enrich information retrieval capabilities among blog posts.
\
We first introduce the corporate context of the study and the issues we have faced that motivated our approach. Then, we argue how the use of domain ontologies combined with the SIOC vocabulary on the top of an existing folksonomy and weblogging platform offers a way to get rid of free-tagging classification flaws, and enhances information retrieval by suggesting related blog posts.
\
Aside of the theoretical background, this paper also focuses on implementation. We present experimental results of this approach through the example of add-ons to a corporate blogging platform and the associated semantic web search engine, that extensively uses RDF and other semantic web technologies to find appropriate information and suggest related posts.	132ef7eee95a8703ad9af4d9b80306af	4a44286e417cf21aab89123e8bc6d51a	b184134a9060ddedb38102bb12556314	inproceedings	{Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs}	Alexandre Passant	\N	2007
7814500	Journal of Artificial Intelligence Research	4	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	reinforcementlearning	\N	\N	CCNLab BibTeX	\N	\N	237-285	\N	\N	\N		\N	61a11b7f875a184f602d70a64dce21a6	c31ee295fcace359e09aeec78cc5d4e9	bb00bfaebd7491f84f0953abc0662ca9	article	Reinforcement learning: A survey	L. P. Kaelbling and M. L. Littman and A. W. Moore	\N	1996
7814735	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Irvine, California	{University of California, Irvine}	\N	Fiel00	http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm	\N	Famous dissertation of R. T. Fielding.	\N	\N	\N	\N	\N	\N	topic = {rest[1]}, uri = {http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm}	The World Wide Web has succeeded in large part because its software architecture has been designed to meet the needs of an Internet-scale distributed hypermedia system. The Web has been iteratively developed over the past ten years through a series of modifications to the standards that define its architecture. In order to identify those aspects of the Web that needed improvement and avoid undesirable modifications, a model for the modern Web architecture was needed to guide its design, definition, and deployment. Software architecture research investigates methods for determining how best to partition a system, how components identify and communicate with each other, how information is communicated, how elements of a system can evolve independently, and how all of the above can be described using formal and informal notations. My work is motivated by the desire to understand and evaluate the architectural design of network-based application software through principled use of architectural constraints, thereby obtaining the functional, performance, and social properties desired of an architecture. An architectural style is a named, coordinated set of architectural constraints. This dissertation defines a framework for understanding software architecture via architectural styles and demonstrates how styles can be used to guide the architectural design of network-based application software. A survey of architectural styles for network-based applications is used to classify styles according to the architectural properties they induce on an architecture for distributed hypermedia. I then introduce the Representational State Transfer (REST) architectural style and describe how REST has been used to guide the design and development of the architecture for the modern Web. REST emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. I describe the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, contrasting them to the constraints of other architectural styles. Finally, I describe the lessons learned from applying REST to the design of the Hypertext Transfer Protocol and Uniform Resource Identifier standards, and from their subsequent deployment in Web client and server software.	f4bf184c8939702e1b02818a432201b9	833ffb7e240120036017a40097125555	c865fb78abb41c54c78fdfe6c03de736	phdthesis	Architectural Styles and the Design of Network-based Software Architectures	Roy Thomas Fielding	\N	2000
7814900	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Scerri2008semanticemail	\N	\N		\N	\N	\N	\N	\N	\N		In this paper we provide a summary of work that has been
\
pursued in the area of Semantic Email, with a particular
\
focus on our work in the area. The aim of this paper is to
\
provide a status quo for this topic, as well as to generate
\
ideas and discussions that could evolve the topic and take it
\
to new heights. We finish off by outlining future directions
\
for evaluation, improvement as well as extension of our
\
current technologies.	c8ffa671c10aacd05a0ff25d06529751	1be28487c5478a5b8e34ed4905365147	a815716e0ed34426ca5e3a62b89d5d6c	inproceedings	The path towards Semantic Email: Summary and Outlook	Simon Scerri Brian Davis Siegfried Handschuh	\N	2008
7814907	The Semantic Web: Research and Applications	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Sintek2007	http://dx.doi.org/10.1007/978-3-540-72667-8\\_42	\N		\N	\N	594--608	\N	\N	\N	posted-at = {2007-07-13 13:06:50}, citeulike-article-id = {1454012}, priority = {0}, doi = {http://dx.doi.org/10.1007/978-3-540-72667-8\\_42}	The vision of the Social Semantic Desktop defines a user’s personal information environment as a source and end-point of the Semantic Web: Knowledge workers comprehensively express their information and data with respect to their own conceptualizations. Semantic Web languages and protocols are used to formalize these conceptualizations and for coordinating local and global information access. From the way this vision is being pursued in the NEPOMUK project, we identified several requirements and research questions with respect to knowledge representation. In addition to the general question of the expressivity needed in such a scenario, two main challenges come into focus: i) How can we cope with the heterogeneity of knowledge models and ontologies, esp. multiple knowledge modules with potentially different interpretations? ii) How can we support the tailoring of ontologies towards different needs in various exploiting applications? In this paper, we present NRL, an approach to these two question that is based on named graphs for the modularization aspect and a view concept for the tailoring of ontologies. This view concept turned out to be of additional value, as it also provides a mechanism to impose different semantics on the same syntactical structure. We think that the elements of our approach are not only adequate for the semantic desktop scenario, but are also of importance as building blocks for the general Semantic Web.	0140d0e8f21253c2b04cf37389c8d5a3	6cbc129f16a698cc00bc1cc971dd824b	a24c86f5f43bd907dd41b19fd405270f	incollection	Distributed Knowledge Representation on the Social Semantic Desktop: Named Graphs, Views and Roles in NRL	Michael Sintek and Ludger van Elst and Simon Scerri and Siegfried Handschuh	\N	2007
7814912	\N	5021	\N	\N	\N	\N	ESWC	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	Scerri2008Semantic	http://dx.doi.org/10.1007/978-3-540-68234-9\\_12	\N		\N	\N	124--138	\N	\N	\N	posted-at = {2009-02-11 14:09:07}, citeulike-article-id = {4035228}, priority = {2}, isbn = {978-3-540-68233-2}, doi = {http://dx.doi.org/10.1007/978-3-540-68234-9\\_12}	\N	14fa6ecd1f193332fd5e3486141ef39e	00c6ed428990b0a3d5539068e7213105	80bd60f8de1c126ad343ec3d756555ae	inproceedings	Semantic Email as a Communication Medium for the Social Semantic Desktop	Simon Scerri and Siegfried Handschuh and Stefan Decker	Sean Bechhofer and Manfred Hauswirth and J\\"org Hoffmann and Manolis Koubarakis and Sean Bechhofer and Manfred Hauswirth and J\\"org Hoffmann and Manolis Koubarakis	2008
7814917	\N	315	\N	\N	\N	\N	WoMO	\N	\N	\N	CEUR-WS.org	\N	\N	CEUR Workshop Proceedings	conf/kcap/SintekEGSH07	http://dblp.uni-trier.de/db/conf/kcap/womo2007.html#SintekEGSH07	\N	dblp	\N	\N	\N	\N	\N	conf/kcap/2007womo	date = {2009-02-15}, ee = {http://ceur-ws.org/Vol-315/paper8.pdf}	\N	ff8b95f156a2bb7176aa431181a5a3ad	90c073ab64664ff807861c7360aef248	92200fa05960c556efb5f6ccc30d1690	inproceedings	Knowledge Representation for the Distributed, Social SemanticWeb - Named Graphs, Graph Roles and Views in NRL.	Michael Sintek and Ludger van Elst and Gunnar Aastrand Grimnes and Simon Scerri and Siegfried Handschuh	Bernardo Cuenca Grau and Vasant Honavar and Anne Schlicht and Frank Wolter	2007
7815012	\N	\N	\N	\N	\N	\N	KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining	\N	\N	\N	ACM	New York, NY, USA	\N	\N	McGlohon2008	http://portal.acm.org/citation.cfm?id=1401890.1401955&coll=Portal&dl=GUIDE&CFID=27533196&CFTOKEN=53283800	\N	Weighted graphs and disconnected components	\N	\N	524--532	\N	\N	\N	location = {Las Vegas, Nevada, USA}, isbn = {978-1-60558-193-4}, doi = {http://doi.acm.org/10.1145/1401890.1401955}	\N	db22dc48f53dc7f7bd4015dcd1006918	5c159f10484cf7739246c58e3477d9d3	ce3bc25c126d94cb8f5874b2581ca3c8	inproceedings	Weighted graphs and disconnected components: patterns and a generator	Mary McGlohon and Leman Akoglu and Christos Faloutsos	\N	2008
7815461	\N	\N	\N	\N	\N	\N	CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management	\N	\N	\N	ACM	New York, NY, USA	\N	\N	584877	http://portal.acm.org/citation.cfm?id=584792.584877	\N	Evaluation of hierarchical clustering algorithms for document datasets	\N	\N	515--524	\N	\N	\N	location = {McLean, Virginia, USA}, isbn = {1-58113-492-4}, doi = {http://doi.acm.org/10.1145/584792.584877}	Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering solutions provide a view of the data at different levels of granularity, making them ideal for people to visualize and interactively explore large document collections.In this paper we evaluate different partitional and agglomerative approaches for hierarchical clustering. Our experimental evaluation showed that partitional algorithms always lead to better clustering solutions than agglomerative algorithms, which suggests that partitional clustering algorithms are well-suited for clustering large document datasets due to not only their relatively low computational requirements, but also comparable or even better clustering performance. We present a new class of clustering algorithms called constrained agglomerative algorithms that combine the features of both partitional and agglomerative algorithms. Our experimental results showed that they consistently lead to better hierarchical solutions than agglomerative or partitional algorithms alone.	322faad90245114300115371e908d0c6	b515af013b3884afd7858afbfe69692d	55a82d53b555b2c2c3485c1d9cf1b3d2	inproceedings	Evaluation of hierarchical clustering algorithms for document datasets	Ying Zhao and George Karypis	\N	2002
7815464	\N	\N	\N	\N	\N	\N	In KDD Workshop on Text Mining	\N	\N	\N	\N	\N	\N	\N	Steinbach00acomparison	\N	\N	CiteSeerX — A comparison of document clustering techniques	\N	\N	\N	\N	\N	\N		\N	e79503a31f20b2b6df36ddbc5111b072	3340fbf75ada2ccb45a50dd5832f5f07	d4e6014e6fffec76912c7cd3cbfb181b	inproceedings	A comparison of document clustering techniques	Michael Steinbach and George Karypis and Vipin Kumar	\N	2000
7815894	Psychological review	104	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	landauer_solution_1997	\N	\N		\N	\N	211--240	\N	2	\N		\N	6adae0e25b2f33a13bfc9621048246ca	62e934c46a819d075cf9e49a0646b84e	64a15051245f4da439e8c315d8edd681	article	A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge	{TK} {LANDAUER} and {ST} {DUMAIS}	\N	1997
7816610	ACM Trans. Knowl. Discov. Data	1	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Leskovec2007	http://portal.acm.org/citation.cfm?id=1217299.1217301&coll=Portal&dl=GUIDE&CFID=27638561&CFTOKEN=91645549	\N	Graph evolution	\N	\N	2	\N	1	\N	issn = {1556-4681}, doi = {http://doi.acm.org/10.1145/1217299.1217301}	\N	8a0011d0f0844331647f97b55dc474af	d7fc24f7d23df0c4b7093b0ce0678fab	e332e869af78eea134db6c0daa3a13cc	article	Graph evolution: Densification and shrinking diameters	Jure Leskovec and Jon Kleinberg and Christos Faloutsos	\N	2007
7816989	California Management Review	44	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	citeulike:358682	https://webapp.comm.virginia.edu/networkroundtable/portals/0/making_invisible_work_visible.pdf	\N		\N	\N	25--46	\N	2	\N	citeulike-article-id = {358682}, priority = {2}	With efforts to de-layer organizations and reduce functional boundaries, coordination and
\
work of importance increasingly occur through networks of informal relations rather than
\
channels tightly prescribed by formal reporting structures or detailed work processes.
\
However, while organizations are moving to network forms through joint ventures,
\
alliances and other collaborative relationships executives generally pay little attention to
\
assessing and supporting informal networks within their own organizations. Working
\
with a consortium of 23 companies over the past eighteen months we have found social
\
network analysis a valuable means of facilitating collaboration in strategically important
\
groups such as top leadership networks, strategic business units, new product
\
development teams, communities of practice, joint ventures and mergers. This article
\
outlines how social network analysis can be effective in: 1) Promoting collaboration
\
within a strategically important group; 2) Supporting critical junctures in networks that
\
cross functional, hierarchical or geographic boundaries and 3) Ensuring integration of a
\
network following restructuring or other strategic change initiatives. By making informal
\
networks visible, social network analysis helps managers systematically assess and
\
support strategically important collaboration.	979fbe676d8fdc1728f262ae926693c6	d29cdee6f69bd7da3465b41094ec39c4	c14dd70947bbded1ace01e88dc8bfc28	article	Making invisible work visible: Using social network analysis to support strategic collaboration	Rob Cross and Stephen P. Borgatti and Andrew Parker	\N	2002
7817002	AI Communications	21	\N	\N	\N	\N	\N	\N	\N	\N	IOS Press	Amsterdam	\N	\N	jaeschke2008tag	http://dx.doi.org/10.3233/AIC-2008-0438	\N		\N	\N	231-247	\N	4	\N	issn = {0921-7126}, vgwort = {63}, doi = {10.3233/AIC-2008-0438}	Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
\
In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of
\
user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences.  We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
\
	9e9f1e49306d787521a1ca4c336ef787	b2f1aba6829affc85d852ea93a8e39f7	955bcf14f3272ba6eaf3dadbef6c0b10	article	Tag Recommendations in Social Bookmarking Systems	Robert Jäschke and Leandro Marinho and Andreas Hotho and Lars Schmidt-Thieme and Gerd Stumme	Enrico Giunchiglia	2008
7822951	\N	\N	\N	\N	\N	\N	CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems	\N	\N	\N	ACM	New York, NY, USA	\N	\N	1124915	http://doi.acm.org/10.1145/1124772.1124915	\N	Motivating participation by displaying the value of contribution	\N	\N	955--958	\N	\N	\N	location = {Montr\\'{e}al, Qu\\'{e}bec, Canada}, isbn = {1-59593-372-7}, doi = {http://doi.acm.org/10.1145/1124772.1124915}	One of the important challenges faced by designers of online communities is eliciting sufficent contributions from community members. Users in online communities may have difficulty either in finding opportunities to add value, or in understanding the value of their contributions to the community. Various social science theories suggest that showing users different perspectives on the value they add to the community will lead to differing amounts of contribution. The present study investigates a design augmentation for an existing community Web site that could benefit from additional contribution. The augmented interface includes individualized opportunities for contribution and an estimate of the value of each contribution to the community. The value is computed in one of four different ways: (1) value to self; (2) value to a small group the user has affinity with; (3) value to a small group the user does not have affinity with; and (4) value to the entire user community. The study compares the effectiveness of the different notions of value to 160 community members.	e23d2f5a50eda2f09f82c3e52bf2a275	b116c2e738160dda383db1798f2c70b4	4e48e625f90e440985aeb536823d4733	inproceedings	Motivating participation by displaying the value of contribution	Al M. Rashid and Kimberly Ling and Regina D. Tassone and Paul Resnick and Robert Kraut and John Riedl	\N	2006
7831544	\N	\N	\N	\N	\N	\N	KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining	\N	\N	\N	ACM	New York, NY, USA	\N	\N	312186	http://portal.acm.org/citation.cfm?id=312129.312186	\N	Fast and effective text mining using linear-time document clustering	\N	\N	16--22	\N	\N	\N	location = {San Diego, California, United States}, isbn = {1-58113-143-7}, doi = {http://doi.acm.org/10.1145/312129.312186}	\N	8609a0ae77bc9adaa01553045bb99a33	03b9c1db32172b54d58716e324e63511	794e447495e9319dd1e9f660db509b53	inproceedings	Fast and effective text mining using linear-time document clustering	Bjornar Larsen and Chinatsu Aone	\N	1999
7833437	\N	\N	\N	\N	\N	\N	Proceedings of I-Semantics ’08, International Conference on Semantic Systems	\N	\N	\N	\N	\N	\N	\N	Weller2008	http://wwwalt.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/35/1221222331triple-i_t.pdf	\N		\N	\N	100-117	\N	\N	\N		As social tagging applications continuously gain in popularity, it becomes more and more accepted that models and tools for (re-)organizing tags are needed. Some first approaches are already practically implemented. Recently, activities to edit and organize tags have been described as “tag gardening”. We discuss different ways to subsequently revise and reedit tags and thus introduce different “gardening activities”; among them models that allow gradually adding semantic structures to folksonomies and/or that combine them with more complex forms of knowledge organization systems.	eb30d655ef4322f87b07f1e7d9d473a8	4bdfc0ff40e7790e4c91582171c52242	fdae3293f71d88d9dacac0b3d635fb45	inproceedings	Seeding, Weeding, Fertilizing. Different Tag Gardening Activities for Folksonomy Maintenance and Enrichment.	Katrin Weller and Isabella Peters	S. Auer and S. Schaffert and T. Pellegrini	2008
7835296	Machine Learning	5	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Schapire:90	\N	\N		\N	\N	197--227	\N	\N	\N	priority = {2}, citeulike-article-id = {2381491}	\N	4c9788ec67d63f288bd675eb2231e506	9d43cf373e136baedbe868305bcde631	4b54d67152e9e88f2280f216179da34f	article	The Strength of Weak Learnability	R. E. Schapire	\N	1990
7835667	The SMART retrieval system: experiments in automatic document processing	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	rocchio1971rfi	\N	\N		\N	\N	313--323	\N	\N	\N		\N	4f39b93b65e7970462ddcedc9f268843	c18d843e34fe4f8bd1d2438227857225	fc6b8550bc2e30ece2fdf484139148f5	article	{Relevance feedback in information retrieval}	J.J. Rocchio	\N	1971
7837585	Journal of the American Society for Information Science	27	\N	\N	\N	\N	\N	\N	\N	\N	\N	School of Library, Archive and Information Studies University College London London WC1E 6BT, England; Computer Laboratory University of Cambridge Cambridge CB2 3QG, England	\N	\N	Robertson-Relevance-1976	http://dx.doi.org/10.1002/asi.4630270302	\N		\N	\N	129--146	\N	3	\N	posted-at = {2007-10-30 11:03:54}, citeulike-article-id = {1839956}, priority = {4}, doi = {10.1002/asi.4630270302}	This paper examines statistical techniques for exploiting relevance information to weight search terms. These  techniques are presented as a natural extension of weighting methods using information about the distribution of index terms in documents in general. A series of relevance weighting functions is derived and is justified by theoretical considerations. In particular, it is shown that specific weighted search methods are implied by a general probabilistic theory of retrieval. Different applications of relevance weighting are illustrated by experimental results for test collections.	fd82e7e42f89ab27f0989b9507bbd0d0	67e5814e51aa3fdddadc2e8274bcb03d	0b1b36aa39c0f00d286a56054d56aee5	article	Relevance weighting of search terms	S. E. Robertson and Sparck K. Jones	\N	1976
7837609	\N	\N	\N	\N	\N	\N	Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval	\N	\N	ACM New York, NY, USA	\N	\N	\N	\N	ponte1998lma	\N	\N		\N	\N	275--281	\N	\N	\N		\N	9285c9274a15bdf5a5f29c1acef403f4	7d5d602886fa34e485cf6194f70bd793	229b65aa2b99b2f27bc990840e79b3eb	article	{A language modeling approach to information retrieval}	J.M. Ponte and W.B. Croft	\N	1998
7837887	Communications of the ACM	47	\N	\N	December	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cayz04	\N	\N		\N	\N	47-52	\N	12	\N	timestamp = {2006.01.20}, file = {Cayz04.pdf:Cayz04.pdf:PDF}, owner = {ritterskamp}, comment = {Need for decentralized, informal Knowledge Management
\
\	
\
\	Diss: Anforderung nach "Ease of use and Capture" unterst�tzt die Forderung
\
\	nach der Minimierung von Reibungsverlusten beim Working Sphere Multitasking.
\
\	Dar�ber hinaus scheint der kollaborative Umgang mit Information Snippets
\
\	(und damit das dezentrale, informelle Knowledge Management) eine
\
\	eigene Working Sphere darzustellen - es mag somit als Fallbeispiel
\
\	/ Szenario dienen!
\
\	
\
\	Transient Nature of EMail means that information snippets (= small
\
\	items of interest that shall be annotated, shared and stored; possibly
\
\	building blocks of shard knowledge) are effectively lost over time.
\
\	
\
\	ggf. Auswahl WiMa 2006: Alternative, innovative Technologien des Knowledge
\
\	Management}	\N	484ad1e33465bc4a7d25408a4c3cd8ce	a95043a1f0fa89990e827162c964207d	5559fb7069612c1eb3b0ed7211ac5532	article	Semantic Blogging and decentralized Knowledge Management	S. Cayzer	\N	2004
7837910	\N	\N	\N	\N	\N	\N	WWW '06: Proceedings of the 15th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	GMP06	http://portal.acm.org/citation.cfm?id=1135777.1135809	\N	We present a tool focused on this audience–a system that addresses the very large scale information gathering, filtering and routing, and presentation problems associated with creating a useful incremental stream of information from the web as a whole	\N	\N	183-192	\N	\N	\N	location = {Edinburgh, Scotland}, isbn = {1-59593-323-9}, doi = {http://doi.acm.org/10.1145/1135777.1135809}	Popularity based search engines have served to stagnate information retrieval from the web. Developed to deal with the very real problem of degrading quality within keyword based search they have had the unintended side effect of creating "icebergs" around topics, where only a small minority of the information is above the popularity water-line. This problem is especially pronounced with emerging information--new sites are often hidden until they become popular enough to be considered above the water-line. In domains new to a user this is often helpful--they can focus on popular sites first. Unfortunately it is not the best tool for a professional seeking to keep up-to-date with a topic as it emerges and evolves.We present a tool focused on this audience--a system that addresses the very large scale information gathering, filtering and routing, and presentation problems associated with creating a useful incremental stream of information from the web as a whole. Utilizing the WebFountain platform as the primary data engine and Really Simple Syndication (RSS) as the delivery mechanism, our "Daily Deltas" (Delta) application is able to provide an informative feed of relevant content directly to a user. Individuals receive a personalized, incremental feed of pages related to their topic allowing them to track their interests independent of the overall popularity of the topic.	85ef2f390ce67d8f338e853f79b24e00	6e10dabf43c03fceab08a3b01ecad442	7e7b41a5ef28af9f55f823c1de54c9cf	inproceedings	The web beyond popularity: a really simple system for web scale RSS	Daniel Gruhl and Daniel N. Meredith and Jan H. Pieper and Alex Cozzi and Stephen Dill	\N	2006
7838082	\N	\N	\N	\N	\N	\N	CHI '06: CHI '06 extended abstracts on Human factors in computing systems	\N	\N	\N	ACM	New York, NY, USA	\N	\N	furnas2006	http://portal.acm.org/citation.cfm?id=1125451.1125462	\N	Why do tagging systems work?	\N	\N	36--39	\N	\N	\N	location = {Montr\\'{e}al, Qu\\'{e}bec, Canada}, isbn = {1-59593-298-4}, doi = {http://doi.acm.org/10.1145/1125451.1125462}	The panel will explore the relevance of the emerging tagging systems (Flickr, Del.icio.us, RawSugar and more). Why do they seem to work? What kinds of incentives are required for users to participate? Will tagging survive and scale to mass adoption? What are the behavioral, economic, and social models that underlie each tagging system? What are the dynamics of those systems, and how are they derived from the specific application's design and affordances?.We will demand answers to these questions and others from some of the pioneering practitioners and academics in the field. Bring your wireless laptop to participate in a live tagging experiment! The experiment results will be shown and discussed at the end of the panel. To add to the fun, parts of the discussion will be motivated by short video segments.	c5da2ee16eb484459be66b3ed4585f9c	8c5d2cf29b2103d8baf792f797f3240e	9ba7e51179866fcbda9351d39149df5d	inproceedings	Why do tagging systems work?	G. W. Furnas and C. Fake and L. von Ahn and J. Schachter and S. Golder and K. Fox and M. Davis and C. Marlow and M. Naaman	\N	2006
7838277	\N	3789	\N	\N	\N	\N	MICAI: Fourth Mexican International Conference on Artificial Intelligence	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	Espinosa05	http://dblp.uni-trier.de/db/conf/micai/micai2005.html#EspinosaL05	\N		\N	\N	61-69	\N	\N	conf/micai/2005	date = {2006-05-08}, ee = {http://dx.doi.org/10.1007/11579427_7}, isbn = {3-540-29896-7}	\N	14e11ecf3ece027e8b77b98e273ea7d0	95ac795a40235cdd50dd53638a3099de	836390fc1dc8e6186b34caa3a0223e83	inproceedings	EventNet: Inferring Temporal Relations Between Commonsense Events	J. Espinosa and H. Lieberman	A. F. Gelbukh and A. de Albornoz and H. Terashima-Marín	2005
7838289	Discourse Processes	25	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	landauer1998	http://lsa.colorado.edu/papers/dp1.LSAintro.pdf	\N		\N	\N	259--284	\N	\N	\N		Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSAs reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates wordword and passageword lexical priming data; and, as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.	2822046a72a85a712e7baba21ac4974f	60c2cae5093c82d65be9f2e516da9b29	40f15f30c9d82349092dde6190dc46aa	article	An Introduction to Latent Semantic Analysis	T. K. Landauer and P. W. Foltz and D. Laham	\N	1998
7851893	ACM Transactions on Information Systems	22	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	herlocker2004ecf	\N	\N		\N	\N	5--53	\N	1	\N		\N	59514569bad7a3a0aa2c48927fad5a52	f8a70731d983634ac7105896d101c9d2	2ed91ee4c94d0a30e3f77dde9de36f66	article	{Evaluating collaborative filtering recommender systems}	J.L. Herlocker and J.A. Konstan and L.G. Terveen and J.T. Riedl	\N	2004
7852593	\N	\N	11	\N	\N	\N	Advances in Kernel Methods - Support Vector Learning	\N	\N	\N	MIT Press	Cambridge, MA	\N	\N	joachims99a	http://www.cs.cornell.edu/People/tj/publications/joachims_99a.pdf	\N		\N	\N	169--184	\N	\N	\N		\N	815667b263f6adf34d613dd58c77b0d4	f97179c7ebe10f64411417f9e05563a8	e9c78de662b85b3c8e095027cfa3b7c2	incollection	Making large-Scale {SVM} Learning Practical	Thorsten Joachims	B. Schölkopf and C. Burges and A. Smola	1999
7853527	\N	5318	\N	\N	\N	\N	The Semantic Web - ISWC 2008, Proc.Intl. Semantic Web Conference 2008	\N	\N	\N	Springer	Heidelberg	\N	LNAI	cattuto2008semantic	http://dx.doi.org/10.1007/978-3-540-88564-1_39	\N		\N	\N	615--631	\N	\N	\N		Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks
\
like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.	81b8a42c8600cadaf289d5f91fae41b6	b44538648cfd476d6c94e30bc6626c86	25f7c308c1fc943215af306e76e458b0	inproceedings	Semantic Grounding of Tag Relatedness in Social Bookmarking Systems	Ciro Cattuto and Dominik Benz and Andreas Hotho and Gerd Stumme	Amit P. Sheth and and Steffen Staab and and Mike Dean and and Massimo Paolucci and and Diana Maynard and and Timothy W. Finin and and Krishnaprasad Thirunarayan	2008
7860826	\N	175	\N	\N	November	\N	Proceedings of the 1st Workshop on The Semantic Desktop at the ISWC 2005 Conference	\N	\N	\N	CEUR-WS	\N	\N	CEUR Workshop Proceedings	Sauermann+2005d	\N	\N	My example bibtex entry.	\N	\N	1--19	\N	\N	\N	file = {Sauermann+2005d.pdf:Sauermann+2005d.pdf:PDF}, owner = {Sauermann}	\N	ad3389b1b63e7d42a2a267fa3e7f7c7a	c8f7063ad1eed158b141ce5dc43f922d	a7512bcb7da1f4de855165fbf874660c	inproceedings	{Overview and Outlook on the Semantic Desktop}	Leo Sauermann and Ansgar Bernardi and Andreas Dengel	Stefan Decker and Jack Park and Dennis Quan and Leo Sauermann	2005
7860884	\N	\N	\N	\N	July	\N	\N	World wide web design issues	\N	\N	\N	\N	\N	\N	Berners-Lee2006a	http://www.w3.org/DesignIssues/LinkedData.html	\N		\N	\N	\N	\N	\N	\N	timestamp = {2007.03.21}, owner = {moustaki}	\N	cbcd26de1656f48b11c5f59da3e8ae84	a70519b9adcccb3c2e0d25f99ae1a0b4	71be3a4f180ee7cad3f65f9008070642	misc	Linked Data	Tim Berners-Lee	\N	2006
7861444	\N	\N	\N	\N	May	\N	Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006	\N	\N	\N	\N	Edinburgh, Scotland	\N	\N	xu2006	http://.inf.unisi.ch/phd/mesnage/site/Readings/Readings.html	\N		\N	\N	\N	\N	\N	\N	pdf = {xu06-towards.pdf}, lastname = {Xu}, lastdatemodified = {2006-07-17}, read = {readnext}, own = {own}	Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.	db3540a21a777f5575eaa80091e51924	e18fd92b0ffa21b9f0cbb3a2fe15b873	9a9a66df007b4b59213537ab18404170	inproceedings	Towards the Semantic Web: Collaborative Tag Suggestions	Z. Xu and Y. Fu and J. Mao and D. Su	\N	2006
7865040	Proc.\\ National Academy of Sciences	99	\N	\N	April	\N	\N	\N	\N	\N	\N	\N	\N	\N	pennock2002winners	\N	\N	specific subnetworks within the network can have other distributions [than power-law]	\N	\N	5207--5211	\N	8	\N	misc = {comment = {Lokal vorhanden; PLOD-Algorithmus -> Faloutsos}}	\N	23a4cca2c8dcc4bac7d035a2ac4901e6	1a0fa8a805c65f5a4096627c1e019da4	10554994432471894ca93bd8a0493e17	article	Winners don't take all: Characterizing the
\
                  competition for links on the web	David Pennock and Gary Flake and Steve Lawrence and Eric Glover and C. Lee Giles	\N	2002
7869076	Arxiv preprint cond-mat/0007235	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	newman2001rga	\N	\N		\N	\N	\N	\N	\N	\N		\N	82e1580ec83e58b81ee8fe394a686a62	706d572ebbb2408b5a4ffa6978579dec	08a607a8657ec747029ecbaf8d9f224f	article	{Random graphs with arbitrary degree distributions and their applications}	MEJ Newman and SH Strogatz and DJ Watts	\N	2001
7869390	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Blackwell	\N	\N	\N	wittgenstein53	\N	\N		\N	Translated by G.E.M. Anscombe	\N	Wittgenstein	\N	\N		\N	f3ba39f6a36475a0de61ab72fb1dcd5b	ff9819fd63cca58a28b07e6c46e83050	b4d2e81ab3f24192708770d7eecb925b	book	Philosophical Investigations	Ludwig Wittgenstein	\N	1953
7878927	\N	\N	\N	\N	Nov	\N	International Software Metrics Symposium	\N	\N	\N	\N	Albuquerque, NM	\N	\N	lehman97sms	http://dx.doi.org/10.1109/METRIC.1997.637156	\N		\N	\N	20-32	\N	\N	\N	issn = {}, doi = {}	The process of E-type software development and evolution has
\
proven most difficult to improve, possibly due to the fact that the
\
process is a multi-input, multi-output system involving feedback at many
\
levels. This observation, first recorded in the early 1970s during an
\
extended study of OS/360 evolution, was recently captured in a FEAST
\
(Feedback, Evolution And Software Technology) hypothesis: a hypothesis
\
being studied in on-going two-year project, FEAST/1. Preliminary
\
conclusions based on a study of a financial transaction system-Logica's
\
Fastwire (FW)-are outlined and compared with those reached during the
\
earlier OS/360 study. The new analysis supports, or better does not
\
contradict, the laws of software evolution, suggesting that the 1970s
\
approach to metric analysis of software evolution is still relevant
\
today. It is hoped that FEAST/1 will provide a foundation for mastering
\
the feedback aspects of the software evolution process, opening up new
\
paths for process modelling and improvement	8711d24b980b60e2de4b414f5a56cdb1	4cacf2e95afeede4f29ec61a038c5e94	5285800d4b04fc5f755cbf50abf18860	inproceedings	Metrics and laws of software evolution-the nineties view	M.M. Lehman and J.F. Ramil and P.D. Wernick and D.E. Perry and W.M. Turski	\N	1997
7880246	\N	\N	\N	\N	\N	\N	Computer Human Intraction 2000 Workshop on the What, Who, Where, \	When, Why and How of Context-Awareness	\N	\N	\N	\N	\N	\N	\N	dey2000	ftp://ftp.cc.gatech.edu/pub/gvu/tr/1999/99-22.pdf	\N	Context-aware business processes	\N	\N	\N	\N	\N	\N	timestamp = {2006.03.31 11:12}, pdf = {HonoursResearch/Dey2000-TowardsABetterUnderstandingOfContextAndContextAwareness.pdf}, owner = {peter}	\N	be7b38e623f58b6855a1f4e1eebcb6af	4bb1b5b9aceb4c72f73c8d533835a8ff	598cacfabb97641e3ac6ac80ad2bfdb3	inproceedings	Towards a better understanding of context and context-awareness	Anind K. Dey and G. D. Abowd	\N	2000
7881182	\N	\N	18	\N	\N	\N	Social Semantic Web	\N	\N	\N	Springer	Berlin, Heidelberg	\N	X.media.press	hotho2008social	http://dx.doi.org/10.1007/978-3-540-72216-8_18	\N	SpringerLink - Buchkapitel	\N	\N	363--391	\N	\N	\N	issn = {1439-3107}, isbn = {978-3-540-72215-1}, doi = {10.1007/978-3-540-72216-8}	BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung
\
der Universit{\\"a}t Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalit{\\"a}t, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.	16b8f07a41097cc4e4fc962eaf809465	79dbca4289cfe913aa7f7eb7e0dccea7	5ccf05a86e7f1a089ae83dd47568e6de	incollection	Social Bookmarking am Beispiel BibSonomy	Andreas Hotho and Robert Jäschke and Dominik Benz and Miranda Grahl and Beate Krause and Christoph Schmitz and Gerd Stumme	Andreas Blumauer and Tassilo Pellegrini	2009
7898890	Communications of the ACM	18	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	salton:1975	\N	\N		\N	The paper where vector space model for IR was introduced	613-–620	Salton et al.	11	\N		In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.	2258c185ca334d41dc10695e7fe7757c	0a4c67f15a4869634d8e5e39ba3e7113	1096b4711e20c4523f8830bb90e2cfe6	article	A Vector Space Model for Automatic Indexing	Gerard Salton and Anita Wong and Chung-Shu Yang	\N	1975
7901169	ACM Trans. Inf. Syst.	22	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	963774	\N	\N		\N	\N	89--115	\N	1	\N	issn = {1046-8188}, doi = {http://doi.acm.org/10.1145/963770.963774}	\N	b9e328099dd2f608e09ab4e31912bee9	ffd4c7560d25f5c0b6f92dbba0bbfc79	a887c9d3b1d49ae260a7b3cd1118d36a	article	Latent semantic models for collaborative filtering	Thomas Hofmann	\N	2004
7901488	AI Magazine	14	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	davis1993knowledge	http://www.aaai.org/aitopics/assets/PDF/AIMag14-01-002.pdf	\N		\N	\N	17--33	\N	1	\N		Although knowledge representation is one of the central and in some ways most familiar concepts in AI, the most fundamental question about it--What is it?--has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, while still others have focused on properties that are important to the notion of representation in general.
\

\
In this paper we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and at times conflicting demands on the properties a representation should have.
\

\
We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field. 	c13240e3dc3e3f9078b4d92603000367	0a9d5e8f1265106c18730053f871e80b	fc0910c9b3d967f5b01ae73d252d66fb	article	What is a Knowledge Representation	Randall Davis and Howard Shrobe and Peter Szolovits	\N	1993
7915168	CoRR	abs/cs/0703109	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	kaser2007tagcloud	http://arxiv.org/abs/cs/0703109	\N		\N	informal publication	\N	\N	\N	\N	date = {2008-01-02}	Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As well, the typical layout does not account for relationships that may be known between tags. This paper presents models and algorithms to improve the display of tag clouds that con- sist of in-line HTML, as well as algorithms that use nested tables to achieve a more general 2-dimensional layout in which tag relationships are considered. The first algorithms leverage prior work in typesetting and rectangle packing, whereas the second group of algorithms leverage prior work in Electronic Design Automation. Experiments show our algorithms can be efficiently implemented and perform well. 	9d808bbaf699ad69c1efdf450bac9d57	cb6ed5e3340cf684ec55299adc65e1a9	56270d1311c066a3852bea23eeb8d484	article	Tag-Cloud Drawing: Algorithms for Cloud Visualization	Owen Kaser and Daniel Lemire	\N	2007
7917579	Web Semantics: Science, Services and Agents on the World Wide Web, \	Selcted Papers from the International Semantic Web Conference, 2004 \	- ISWC, 2004, Hiroshima, Japan, 07-11 November 2004	3	\N	\N	October	\N	\N	\N	\N	\N	\N	\N	\N	\N	KaQu05	http://dx.doi.org/10.1016/j.websem.2005.06.002	\N		\N	\N	147-157	\N	2-3	\N	file = {Karger2005SemanticBlog.pdf:Karger2005SemanticBlog.pdf:PDF}, owner = {casi}	\N	a2646e48531ff7689aa5dd80bc68eed5	3c7f1b3daf21333d54182f9e29c87bef	a72a900c1ace3066957778bba930e9c5	article	What would it mean to blog on the semantic web?	David R. Karger and Dennis Quan	\N	2005
7917606	Web Semantics: Science, Services and Agents on the World Wide Web	5	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Mika07	http://www.sciencedirect.com/science/article/B758F-4MYF67P-1/2/56984a3ddf4632bb98b722551cdb1151	\N	ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Ontologies are us: A unified model of social networks and semantics	\N	Selected Papers from the International Semantic Web Conference, International Semantic Web Conference (ISWC2005)	5 - 15	\N	1	\N	issn = {1570-8268}, doi = {DOI: 10.1016/j.websem.2006.11.002}	In our work the traditional bipartite model of ontologies is extended with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.	e5498a08fdd4e0c4f23aa57edc2a7642	5bba04607af19c94d2438ae13f362649	adc44fb1284b7f5a0de43649ff80c4ac	article	Ontologies are us: A unified model of social networks and semantics	Peter Mika	\N	2007
7917711	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	dpunkt Verlag	Heidelberg	\N	\N	ferber2003information	http://information-retrieval.de/	\N		\N	\N	\N	\N	\N	\N		\N	17381af305c78da007d0aa3fd0fa1dcc	52c1b4ab3e818efef6635eb76b778608	b60dbc902a2e19877aec154fa5747751	book	Information Retrieval: Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web	Reginald Ferber	\N	2003
7917715	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	ACM Press / Addison-Wesley	\N	\N	\N	books/aw/Baeza-YatesR99	http://www.ischool.berkeley.edu/~hearst/irbook/glossary.html	\N	dblp	\N	\N	\N	\N	\N	\N	isbn = {0-201-39829-X}	\N	940cc9315b1bd5ed6740b500b0d12dbc	6f78177742b3c836218aacfc7fc4c43c	16ab70975f635f8d72de82e2ef3ef9de	book	Modern Information Retrieval	Ricardo A. Baeza-Yates and Berthier A. Ribeiro-Neto	\N	1999
7918698	European Journal of Open, Distance and E-Learning (EURODL)	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Dalsgaard2006	http://www.eurodl.org/materials/contrib/2006/Christian_Dalsgaard.htm	\N		\N	\N	\N	\N	\N	\N		\N	fc4432ff5c8d67223104844876a5a94c	e162d9423bb4d578ab44c6b4b1528f39	85d91abf98927211f6e7c46189cae7ed	article	Social software: E-learning beyond learning management systems	Christian Dalsgaard	\N	2006
7924498	\N	\N	\N	\N	\N	\N	CSCW '08: Proceedings of the ACM 2008 conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Rader2008	http://portal.acm.org/citation.cfm?id=1460563.1460601&coll=Portal&dl=GUIDE&CFID=31571793&CFTOKEN=71651308	\N	Influences on tag choices in del.icio.us	\N	\N	239--248	\N	\N	\N	location = {San Diego, CA, USA}, isbn = {978-1-60558-007-4}, doi = {http://doi.acm.org/10.1145/1460563.1460601}	\N	4625ed74c3026a824c91d1bf5d6d01c8	57a333943d95f78b53b96180ce750aa7	fcbe153e756bfd1d44371edc80e38f23	inproceedings	Influences on tag choices in del.icio.us	Emilee Rader and Rick Wash	\N	2008
7954007	\N	\N	\N	\N	\N	\N	GROUP '07: Proceedings of the 2007 international ACM conference on Supporting group work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Farooq2007	http://portal.acm.org/citation.cfm?id=1316677	\N	Evaluating tagging behavior in social bookmarking systems	\N	\N	351--360	\N	\N	\N	location = {Sanibel Island, Florida, USA}, isbn = {978-1-59593-845-9}, doi = {http://doi.acm.org/10.1145/1316624.1316677}	\N	2b6399c6ecf559b4d9506076798cef81	66928ca91bf0d777b848fe6f7a55de20	ab8c33fa8808d9896a945575ac42ffc0	inproceedings	Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics	Umer Farooq and Thomas G. Kannampallil and Yang Song and Craig H. Ganoe and John M. Carroll and Lee Giles	\N	2007
7963269	Advances in Applied Probability	\N	\N	\N	\N	\N	\N	\N	\N	\N	Applied Probability Trust	\N	\N	\N	pitman1996rdd	http://www.jstor.org/stable/1428070	\N		\N	\N	525--539	\N	\N	\N		\N	11adf7caa7237c3cfd39b93d5d9c27ec	0b89d6f1f7d4abaebfadf5cb19291b7a	2379a9e30429b1c52d8058521bc82921	article	{Random discrete distributions invariant under size-biased permutation}	J. Pitman	\N	1996
7967406	MIS Quarterly	23	\N	\N	\N	\N	\N	\N	\N	\N	Management Information Systems Research Center, University of Minnesota	\N	\N	\N	KlMy99	http://www.jstor.org/stable/249410	\N		\N	\N	67--93	\N	1	\N	issn = {02767783}, copyright = {Copyright © 1999 Management Information Systems Research Center, University of Minnesota}, jstor_articletype = {primary_article}, language = {}, jstor_formatteddate = {Mar., 1999}, jstor_issuetitle = {}	This article discusses the conduct and evaluation of interpretive research in information systems. While the conventions for evaluating information systems case studies conducted according to the natural science model of social science are now widely accepted, this is not the case for interpretive field studies. A set of principles for the conduct and evaluation of interpretive field research in information systems is proposed, along with their philosophical rationale. The usefulness of the principles is illustrated by evaluating three published interpretive field studies drawn from the IS research literature. The intention of the paper is to further reflection and debate on the important subject of grounding interpretive research methodology.	469fe518d87488f5ea1bfd46ecd060d1	8961514d1a98be3d6ba89f3d255c0bc1	1f982f5950c2b414552ec0ceee01fb98	article	A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems	Heinz K. Klein and Michael D. Myers	\N	1999
7967450	MIS Quarterly	11	\N	\N	\N	\N	\N	\N	\N	\N	Management Information Systems Research Center, University of Minnesota	\N	\N	\N	1987	http://www.jstor.org/stable/248684	\N	>1000 citations, basic article	\N	\N	369-386	\N	3	\N	issn = {02767783}, copyright = {Copyright © 1987 Management Information Systems Research Center, University of Minnesota}, jstor_articletype = {primary_article}, language = {}, jstor_formatteddate = {Sep., 1987}, jstor_issuetitle = {}	This article defines and discusses one of these qualitative methods - the case research strategy. Suggestions are provided for researchers who wish to undertake research employing this approach. Criteria for the evaluation of case research are established and several characteristics useful for categorizing the studies are identified. A sample of papers drawn from information systems journals is reviewed. The paper concludes with examples of research areas that are particularly well-suited to investigation using the case research approach.	58616582b522b1a801e16339b3fefcc3	fc490c1e31f298b3f73ec5e17053982a	ecb66293f7e4da34af8af0a22dbb8265	article	The Case Research Strategy in Studies of Information Systems	Izak Benbasat and David K. Goldstein and Melissa Mead	\N	1987
7967794	B. I. T. online	\N	\N	\N	\N	\N	\N	\N	\N	\N	Dinges & Frick GmbH - B.I.T.online	Postfach 2009, D-65010 Wiesbaden	\N	\N	plieninger2007	http://www.b-i-t-online.de/heft/2007-03/fach4.htm	\N		\N	\N	223-232	\N	3	\N		Eingangs wird der Internetnutzer von morgen geschildert, welcher selten Webseiten besucht, aber häufig seine Sammlung von Neuigkeitenmeldungen nutzt, die sich per RSS-Feeds automatisch aktualisiert. Quelle solcher Feeds ist oft Soziale Software, welche rechnerunabhängig, servergestützt und überwiegend kostenlos im Netz angeboten wird. Verschiedene Dienste werden vorgestellt und ihre Anwendungsmöglichkeit in Bibliotheken skizziert. Diese Dienste werden noch nicht breit in Bibliotheken eingesetzt, da man zu ihrer Nutzung umdenken muss. Die erforderlichen Skills sind technische Innovationsfähigkeit, Moderationsgeschick, Generierung benutzerorientierter Inhalte, proaktives und flexibles Handeln. Es wird vorgeschlagen, die neuen Dienste zum Kennenlernen selbst zu nutzen, nach und nach selbst anzuwenden und am besten mit Arbeitsgruppen in den Institutionen daran zu arbeiten.	006d369866de579013fd9b885ccc9f45	72e6e49419b5d92c507fa9344cb226e0	ec8cc6605d89c8b4f0ea9de062de4935	article	Never run a changing system? -- Über die Chancen des Einsatzes "Sozialer Software" in der Bibliotheksarbeit	Jürgen Plieninger and Edlef Stabenau and Lambert Heller	\N	2007
7971707	MIS Quarterly	28	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	HMPR04	http://www.hec.unil.ch/yp/HCI/articles/hevner04.pdf	\N		\N	\N	75-105	\N	1	\N		\N	bf1d24a052fd20b1c20250be00a224c2	3cec339fe222585d6886d96242e234a4	3f7c7a2bbfcabc67c4f355b4e6402732	article	Design Science in Information Systems Research	Alan R. Hevner and Salvatore T. March and Jinsoo Park and Sudha Ram	\N	2004
8002504	Scientific American	284	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	berners-lee_semantic_2001	http://www.sciam.com/article.cfm?id=the-semantic-web	\N		\N	\N	34--43	\N	5	\N		\N	9736b2e4f998634994515934450effd3	e87f09446138a81e6478625da97885b6	222934145a71a9d6cfbbb375d4d62c1d	article	The Semantic Web	Tim {Berners-Lee} and James Hendler and Ora Lassila	\N	2001
8007686	Proceedings of the 20th International Joint Conference on Artificial Intelligence	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Gabrilovich07	\N	\N	CiteULike: Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis	\N	\N	6--12	\N	\N	\N	posted-at = {2007-12-21 21:33:21}, priority = {2}, citeulike-article-id = {2157093}	\N	fcdb786da62faf3ad5c836c248b77850	5baf6af4bf58cf3926b39a12edb35e58	2192501096693d972228b7471e845a15	article	Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis	E. Gabrilovich and S. Markovitch	\N	2007
8014410	IEEE Intelligent Systems	23	\N	\N	\N	\N	\N	\N	\N	\N	IEEE Computer Society	Los Alamitos, CA, USA	\N	\N	10.1109/MIS.2008.50	http://doi.ieeecomputersociety.org/10.1109/MIS.2008.50	\N	Digital Library	\N	\N	29-40	\N	3	\N	issn = {1541-1672}, doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2008.50}	One of the most visible trends on the Web is the emergence of Social Web sites, which help people create and gather knowledge by simplifying user contributions via blogs, tagging and folksonomies, wikis, podcasts, and online social networks. Current online-community sites are isolated from one another, like islands in a sea. Various discussions might contain complementary knowledge and discussions—parts of the answer a person is looking for—but people participating in one discussion can't readily access information about related discussions elsewhere. The potential synergies among many sites, communities, and services are expensive to exploit, and their data are difficult and cumbersome to link and reuse. The main reason for this lack of interoperation is that for the most part in the Social Web, common standards still don't exist for knowledge and information exchange and interoperation. However, the Semantic Web effort aims to provide the tools needed to define extensible, flexible standards for this purpose. The Semantic Web technology stack is well defined, enabling the creation of metadata and associated vocabularies. The Semantic Web effort is in an ideal position to make Social Web sites interoperable. Applying Semantic Web frameworks including SIOC (Semantically Interlinked Online Communities) and FOAF (Friend-of-a-Friend) to the Social Web can lead to a Social Semantic Web, creating a network of interlinked and semantically rich knowledge. This article is part of a special issue called Semantic Web Update.	a4109d9633891d6baaa2b01ccb5d3c31	c6d82dc26fa9b2079a06ca7120a86917	1c65d81f8b0d784ce2b27cc9e8f7427b	article	Interlinking the Social Web with Semantics	Uldis Bojārs and John G. Breslin and Vassilios Peristeras and Giovanni Tummarello and Stefan Decker	\N	2008
8018175	\N	\N	\N	\N	\N	\N	Description Logic Handbook	\N	\N	\N	Cambridge University Press	\N	\N	\N	dlhandbook	\N	\N		\N	\N	\N	\N	\N	\N	bibsource = {DBLP, http://dblp.uni-trier.de}, isbn = {0-521-78176-0}	\N	625c6b40b991e238d9fa21ec3adc3ef1	2f372868d92592682a7f7dadae8761e7	a21aed7efc646fc7cc4af6eb4502d41c	book	The Description Logic Handbook: Theory, Implementation, and Applications	\N	Franz Baader and Diego Calvanese and Deborah L. McGuinness and Daniele Nardi and Peter F. Patel-Schneider	2003
8018296	\N	\N	\N	\N	\N	\N	The Description Logic Handbook. Theory, Implementation and Applications	\N	\N	\N	Cambridge University Press	\N	\N	\N	Nardi2003	http://www.inf.unibz.it/~franconi/dl/course/dlhb/dlhb-01.pdf	\N		\N	\N	1-40	\N	\N	\N		This introduction presents the main motivations for the development of Description
\
Logics (DL) as a formalism for representing knowledge, as well as some important
\
basic notions underlying all systems that have been created in the DL tradition.
\
In addition, we provide the reader with an overview of the entire book and some
\
guidelines for reading it.
\
We first address the relationship between Description Logics and earlier seman-
\
tic network and frame systems, which represent the original heritage of the field.
\
We delve into some of the key problems encountered with the older efforts. Subse-
\
quently, we introduce the basic features of Description Logic languages and related
\
reasoning techniques.
\
Description Logic languages are then viewed as the core of knowledge represen-
\
tation systems, considering both the structure of a DL knowledge base and its
\
associated reasoning services. The development of some implemented knowledge
\
representation systems based on Description Logics and the first applications built
\
with such systems are then reviewed.
\
Finally, we address the relationship of Description Logics to other fields of Com-
\
puter Science. We also discuss some extensions of the basic representation language
\
machinery; these include features proposed for incorporation in the formalism that
\
originally arose in implemented systems, and features proposed to cope with the
\
needs of certain application domains.	19498776e043e02b7d83dc6c11673029	e2cc7ceb513a60a6a08717dcece20a66	359240c3363eb468680033548e3246ec	incollection	An Introduction to Description Logics	Daniele Nardi and Ronald J. Brachman	F. Baader and D. Calvanese and D. McGuinness and D. Nardi and P. Patel-Schneider	2003
8018320	Scientific American	284	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Berners-Lee01	http://www.sciam.com/article.cfm?id=the-semantic-web	\N		\N	\N	34--43	\N	5	\N		\N	9736b2e4f998634994515934450effd3	e87f09446138a81e6478625da97885b6	222934145a71a9d6cfbbb375d4d62c1d	article	{The Semantic Web}	Tim {Berners-Lee} and James Hendler and Ora Lassila	\N	2001
8022493	\N	\N	\N	\N	\N	\N	GROUP '07: Proceedings of the 2007 international ACM conference on Supporting group work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	farooq2007	http://portal.acm.org/citation.cfm?id=1316677	\N	Evaluating tagging behavior in social bookmarking systems	\N	\N	351--360	\N	\N	\N	location = {Sanibel Island, Florida, USA}, isbn = {978-1-59593-845-9}, doi = {http://doi.acm.org/10.1145/1316624.1316677}	\N	2b6399c6ecf559b4d9506076798cef81	66928ca91bf0d777b848fe6f7a55de20	ab8c33fa8808d9896a945575ac42ffc0	inproceedings	Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics	Umer Farooq and Thomas G. Kannampallil and Yang Song and Craig H. Ganoe and John M. Carroll and Lee Giles	\N	2007
8030694	\N	\N	\N	\N	\N	\N	CSCW '08: Proceedings of the ACM 2008 conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	rader2008	http://portal.acm.org/citation.cfm?id=1460563.1460601&coll=Portal&dl=GUIDE&CFID=31571793&CFTOKEN=71651308	\N	Influences on tag choices in del.icio.us	\N	\N	239--248	\N	\N	\N	location = {San Diego, CA, USA}, isbn = {978-1-60558-007-4}, doi = {http://doi.acm.org/10.1145/1460563.1460601}	\N	db0a27d6e304a636208ed3f132d3781e	57a333943d95f78b53b96180ce750aa7	519451979991fc066db4d1a71144d1b4	inproceedings	Influences on tag choices in del.icio.us	E. Rader and R. Wash	\N	2008
8042208	Journal of Information Science	32	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	ScottA._Golder04012006	http://jis.sagepub.com/cgi/content/abstract/32/2/198	\N	Usage patterns of collaborative tagging systems -- Golder and Huberman 32 (2): 198 -- Journal of Information Science	\N	\N	198-208	\N	2	\N	doi = {10.1177/0165551506062337}, eprint = {http://jis.sagepub.com/cgi/reprint/32/2/198.pdf}	Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in   popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging  systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in  bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.
\
	a6a02950d3a922d230b91be03aa8340d	df675e16fcba9cd0f6afc5c9f2a8a723	f67d3599f5282425b8e0e5b383d436a0	article	{Usage patterns of collaborative tagging systems}	Scott A. Golder and Bernardo A. Huberman	\N	2006
8046088	\N	\N	\N	\N	\N	\N	SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data	\N	\N	\N	ACM Press	New York, NY, USA	\N	\N	agrawal93	\N	\N	Mining association rules between sets of items in large databases	\N	\N	207--216	\N	\N	\N		\N	2ac61a6741f15860729072ba6fdd52c4	53341ce3e6ce51c3bcf8b0219ec239b5	ca35e602124130b480592b3a55267006	inproceedings	Mining association rules between sets of items in large databases	Rakesh Agrawal and Tomasz Imielinski and Arun Swami	\N	1993
8046270	Computer Networks and ISDN Systems	30	\N	\N	April	\N	\N	\N	\N	\N	\N	\N	\N	\N	brin98anatomy	\N	\N	kdd	\N	\N	107--117	\N	1-7	\N	doi = {10.1016/S0169-7552(98)00110-X}	\N	f060dc6647cd3d2a47eb0fc4750c0e78	1234ad3633d435ef79d8a7f36dafa0a9	fc936cec60b1b7ab69f230f14139e8ab	article	{T}he {A}natomy of a {L}arge-{S}cale {H}ypertextual {W}eb {S}earch {E}ngine	Sergey Brin and Lawrence Page	\N	1998
8050313	ACM Comput. Surv.	35	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	EFGK03	http://portal.acm.org/citation.cfm?doid=857076.857078	\N	The many faces of publish/subscribe	\N	\N	114--131	\N	2	\N	issn = {0360-0300}, doi = {http://doi.acm.org/10.1145/857076.857078}	Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.	e8a3408194febfdaeffed0a77d1f7086	de87af8f67c2da01dfe3288f9ed8c97d	811ddb75c2af9b75cd8ab36986e20faa	article	The many faces of publish/subscribe	Patrick Th. Eugster and Pascal A. Felber and Rachid Guerraoui and Anne-Marie Kermarrec	\N	2003
8056859	Intl. Journal of Computer Vision	60	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Lowe:04	\N	\N		\N	\N	91--110	\N	\N	\N	priority = {2}, citeulike-article-id = {2380683}	\N	8c4c0ca5af135906f3d805ff9de00f74	a1c2b94c96ee2ef15ef53e73b7fd9a8d	757a3bb64f6ad0ada7d77a5baf31ce91	article	Distinctive image features from scale-invariant key-points	D. Lowe	\N	2004
8059244	\N	\N	\N	\N	\N	\N	WWW '05: Proceedings of the 14th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Carroll-2005	http://portal.acm.org/citation.cfm?id=1060745.1060835	\N		\N	\N	613--622	\N	\N	\N	location = {Chiba, Japan}, isbn = {1-59593-046-9}, doi = {http://doi.acm.org/10.1145/1060745.1060835}	The Semantic Web consists of many RDF graphs nameable by URIs. This paper extends the syntax and semantics of RDF to cover such Named Graphs. This enables RDF statements that describe graphs, which is beneficial in many Semantic Web application areas. As a case study, we explore the application area of Semantic Web publishing: Named Graphs allow publishers to communicate assertional intent, and to sign their graphs; information consumers can evaluate specific graphs using task-specific trust policies, and act on information from those Named Graphs that they accept. Graphs are trusted depending on: their content; information about the graph; and the task the user is performing. The extension of RDF to Named Graphs provides a formally defined framework to be a foundation for the Semantic Web trust layer.	55a0983c3f85d55b562f86e88472e1c7	08bc9a46b23bc08268c7ad56629e8075	68f3c5be8ad1f4c4749d2deea203ead5	inproceedings	Named graphs, provenance and trust	Jeremy J. Carroll and Christian Bizer and Pat Hayes and Patrick Stickler	\N	2005
8061442	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	MIT Press	Cambridge, MA	\N	\N	fellbaum1998wel	\N	\N		\N	\N	\N	\N	\N	\N		\N	3479aef93c2240c99e1edbfe8a723c77	42daa1681607dd1d3f3234c605d84ec3	8472b4f9d7f2bfc4a97ffd4a023facc6	book	{WordNet: An Electronic Lexical Database}	\N	Christiane Fellbaum	1998
8067091	\N	\N	\N	\N	\N	\N	Proceedings of the 14th International Conference on Computational Linguistics	\N	\N	\N	\N	Nantes, France	\N	\N	hearst1992	http://www.aclweb.org/anthology-new/C/C92/C92-2082.pdf	\N		\N	\N	\N	\N	\N	\N		\N	3e74402b48681049cbbc05e3f13b0b31	8c1e90c6cc76625c34f20370a1af7ea2	d2524d17dec5cc371233694a9d7c8e2e	inproceedings	Automatic Acquisition of Hyponyms from Large Text Corpora	Marti Hearst	\N	1992
8067152	\N	\N	\N	\N	\N	\N	Advances in Knowledge Discovery and Data Mining	\N	\N	\N	\N	\N	\N	\N	books/mit/fayyadPSU96/FayyadPS96	http://dblp.uni-trier.de/db/books/collections/fayyad96.html#FayyadPS96	\N	dblp	\N	\N	1-34	\N	\N	\N	date = {2002-01-03}	\N	0ebaecbaae85d538e1f249e4f8d37cc3	79663e4b1f464b82ce1ae45345dc424f	e59886c68d1fc9bb4d1a8d6a1a644a60	incollection	From Data Mining to Knowledge Discovery: An Overview.	Usama M. Fayyad and Gregory Piatetsky-Shapiro and Padhraic Smyth	\N	1996
8071927	\N	\N	\N	\N	Februar	\N	\N	\N	\N	\N	\N	\N	\N	\N	Manola-2004	http://www.w3.org/TR/2004/REC-rdf-primer-20040210/	\N		\N	Stand: 15.4.2009	\N	\N	\N	\N		\N	038ea058611c59fa16ab37f2ec89e29b	c70eb989b40c4f552bd2e822d7c03aaf	06f02f777d04367474a23d6f4164cb55	article	RDF Primer	\N	Frank Manola and Eric Miller	2004
8071932	\N	\N	\N	\N	\N	\N	\N	W3C Recommendation	{World Wide Web Consortium (W3C)}	\N	\N	\N	\N	\N	Beckett-2004	\N	Recommendation		\N	Internet: \\url{http://www.w3.org/TR/rdf-syntax/}	\N	\N	\N	\N		\N	225f49e96e7afc0e5497da359e4ef040	175620203c4b621b2a7734ae29a31afb	b91b10dc8620baac5770d66af4490241	techreport	{RDF/XML Syntax Specification (Revised)}	D. Beckett	\N	2004
8072213	\N	\N	\N	\N	February	10	\N	\N	W3C	\N	\N	\N	\N	\N	rdfs	http://www.w3.org/TR/2004/REC-rdf-schema-20040210/	W3C Recommendation		\N	http://www.w3.org/TR/2004/REC-rdf-schema-20040210/	\N	\N	\N	\N		\N	53823941b985569f781a7d6c2d3ff5d5	e40d8e7808392e442b9578f6f244c35d	9f719ac2ddf917c967e4cddd5e64e99b	techreport	RDF Vocabulary Description Language 1.0: RDF Schema	Dan Brickley and Ramanatgan V. Guha	\N	2004
8072225	\N	\N	\N	\N	\N	\N	Proceedings of the 17th International Conference on World Wide Web (WWW)	\N	\N	\N	ACM	\N	\N	\N	Bizer-2008	http://dblp.uni-trier.de/db/conf/www/www2008.html#BizerHIB08	\N	dblp	\N	\N	1265-1266	\N	\N	conf/www/2008	date = {2008-05-13}, ee = {http://doi.acm.org/10.1145/1367497.1367760}, isbn = {978-1-60558-085-2}	\N	d1e3b0d63942ad143c9f364fc7fd5f0a	98bbfb6bedec5433ee2eb247354f2112	bc1bc9fb58c193444e74f3a3b89e337f	inproceedings	Linked data on the web (LDOW2008).	Christian Bizer and Tom Heath and Kingsley Idehen and Tim Berners-Lee	Jinpeng Huai and Robin Chen and Hsiao-Wuen Hon and Yunhao Liu and Wei-Ying Ma and Andrew Tomkins and Xiaodong Zhang	2008
8072231	\N	\N	\N	\N	Juli	\N	\N	\N	\N	\N	\N	\N	\N	\N	Berners-Lee-2006	http://www.w3.org/DesignIssues/LinkedData.html	\N		\N	\N	\N	\N	\N	\N		\N	cbcd26de1656f48b11c5f59da3e8ae84	a70519b9adcccb3c2e0d25f99ae1a0b4	71be3a4f180ee7cad3f65f9008070642	misc	Linked Data	Tim Berners-Lee	\N	2006
8088235	User Modeling and User-Adapted Interaction	12	\N	\N	#nov#	\N	\N	\N	\N	\N	\N	\N	\N	\N	paper:burke:2002	http://dx.doi.org/10.1023/A:1021240730564	\N	SpringerLink - Journal Article	\N	\N	331--370	\N	4	\N		Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
\
ER  -	54053d8f4f8573b1c4e799207b3dadd9	f40020400b8bc08adca29a987caf25d8	460b623792e13b4ec0e990563e57f26c	article	Hybrid Recommender Systems: Survey and Experiments	Robin Burke	\N	2002
8095280	\N	\N	\N	\N	\N	\N	CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Faaborg06	http://portal.acm.org/citation.cfm?id=1124883	\N	A goal-oriented web browser	\N	\N	751--760	\N	\N	\N	location = {Montr\\'{e}al, Qu\\'{e}bec, Canada}, isbn = {1-59593-372-7}, doi = {http://doi.acm.org/10.1145/1124772.1124883}	Many users are familiar with the interesting but limited functionality of Data Detector interfaces like Microsoft's Smart Tags and Google's AutoLink. In this paper we significantly expand the breadth and functionality of this type of user interface through the use of large-scale knowledge bases of semantic information. The result is a Web browser that is able to generate personalized semantic hypertext, providing a goal-oriented browsing experience.We present (1) Creo, a Programming by Example system for the Web that allows users to create a general-purpose procedure with a single example, and (2) Miro, a Data Detector that matches the content of a page to high-level user goals.An evaluation with 34 subjects found that they were more efficient using our system, and that the subjects would use features like these if they were integrated into their Web browser.	a8a8e65a5c8f23eae7adc7494a6ccb99	3adc974b81c3469498e632068f7a5914	ec89958f73d65a2d52e9c57af395c3ed	inproceedings	A goal-oriented web browser	Alexander Faaborg and Henry Lieberman	\N	2006
8096700	\N	\N	\N	\N	\N	\N	Web 2.0 - Eine empirische Bestandsaufnahme	\N	\N	\N	Vieweg+Teubner	Wiesbaden	\N	\N	Schaefer2008	http://www.kooperationssysteme.de/docs/2008-SchaeferRichterKoch-bloganalyse.pdf	\N		\N	\N	53-72	\N	\N	\N		\N	2a35c2ac3ff72e5d1fd7fd3ef3c012b7	b50168d86931994ff5ba43c7ac5dc6a4	9c729163180118618ddfcc315974d508	incollection	Wer bloggt was? Eine Analyse der deutschen Top 100-Blogs mit Hilfe von Cluster-Verfahren	Sebastian Schäfer and Alexander Richter and Michael Koch	Paul Alpar and Steffen Blaschke	2008
8108468	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Cattuto2006	http://arxiv.org/abs/cs/0605015	\N	Collaborative Tagging and Semiotic Dynamics	\N	cite arxiv:cs/0605015
\
Comment: 8 pages, 7 figures	\N	\N	\N	\N		  Collaborative tagging has been quickly gaining ground because of its ability
\
to recruit the activity of web users into effectively organizing and sharing
\
vast amounts of information. Here we collect data from a popular system and
\
investigate the statistical properties of tag co-occurrence. We introduce a
\
stochastic model of user behavior embodying two main aspects of collaborative
\
tagging: (i) a frequency-bias mechanism related to the idea that users are
\
exposed to each other's tagging activity; (ii) a notion of memory - or aging of
\
resources - in the form of a heavy-tailed access to the past state of the
\
system. Remarkably, our simple modeling is able to account quantitatively for
\
the observed experimental features, with a surprisingly high accuracy. This
\
points in the direction of a universal behavior of users, who - despite the
\
complexity of their own cognitive processes and the uncoordinated and selfish
\
nature of their tagging activity - appear to follow simple activity patterns.
\
	845345cbe77d7c698fcc048b61725c75	59b1bd0ed96f41d2c3c98ff232df5dd2	8d265ea13915a79ec08fe13b8e7074c7	misc	Collaborative Tagging and Semiotic Dynamics	Ciro Cattuto and Vittorio Loreto and Luciano Pietronero	\N	2006
8121356	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	N3	http://www.w3.org/DesignIssues/Notation3	\N		\N	See \\url{http://www.w3.org/DesignIssues/Notation3.html}	\N	\N	\N	\N		\N	b62fd6b46d944d523424808ff2af6de2	37d752500af6c439394c88d433eef517	29c577a1ea1fab4bad4c6e78e879f9f6	misc	Notation 3	Tim Berners-Lee	\N	1998
8121582	ACM Comput. Surv.	37	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	mernik05	http://portal.acm.org/citation.cfm?doid=1118890.1118892	\N	When and how to develop domain-specific languages	\N	\N	316--344	\N	4	\N	issn = {0360-0300}, doi = {http://doi.acm.org/10.1145/1118890.1118892}	Domain-specific languages (DSLs) are languages tailored to a specific application domain. They offer substantial gains in expressiveness and ease of use compared with general-purpose programming languages in their domain of application. DSL development is hard, requiring both domain knowledge and language development expertise. Few people have both. Not surprisingly, the decision to develop a DSL is often postponed indefinitely, if considered at all, and most DSLs never get beyond the application library stage.Although many articles have been written on the development of particular DSLs, there is very limited literature on DSL development methodologies and many questions remain regarding when and how to develop a DSL. To aid the DSL developer, we identify patterns in the decision, analysis, design, and implementation phases of DSL development. Our patterns improve and extend earlier work on DSL design patterns. We also discuss domain analysis tools and language development systems that may help to speed up DSL development. Finally, we present a number of open problems.	5514eeb77bccc7490de72198e7167bbd	2a6d877a9dfd27b12191063ff1a7208e	5aac369aac673a943dbd5759a8c4d7e4	article	When and how to develop domain-specific languages	Marjan Mernik and Jan Heering and Anthony M. Sloane	\N	2005
8127074	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Springer	Berlin [u.a.]	\N	\N	Hitzler2008	http://dx.doi.org/10.1007/978-3-540-33994-6	\N	SpringerLink - Buch	\N	\N	\N	\N	\N	\N		\N	dabc4b84d35369da648c4d03de41246e	db1caa118f8ad9115bd4990369e2cf6f	1f49353eb5f205362dac1fea931c250d	book	Semantic Web -- Grundlagen	Pascal Hitzler and Markus Krötzsch and Sebastian Rudolph and York ER Sure	\N	2008
8127294	\N	\N	\N	\N	\N	\N	HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia	\N	\N	\N	ACM	New York, NY, USA	\N	\N	krause2008logsonomy	http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia	\N		\N	\N	157--166	\N	\N	\N	location = {Pittsburgh, PA, USA}, isbn = {978-1-59593-985-2}, vgwort = {17}, doi = {http://doi.acm.org/10.1145/1379092.1379123}	Social bookmarking systems constitute an established
\
part of the Web 2.0. In such systems
\
users describe bookmarks by keywords
\
called tags. The structure behind these social
\
systems, called folksonomies, can be viewed
\
as a tripartite hypergraph of user, tag and resource
\
nodes. This underlying network shows
\
specific structural properties that explain its
\
growth and the possibility of serendipitous
\
exploration.
\
Today’s search engines represent the gateway
\
to retrieve information from the World Wide
\
Web. Short queries typically consisting of
\
two to three words describe a user’s information
\
need. In response to the displayed
\
results of the search engine, users click on
\
the links of the result page as they expect
\
the answer to be of relevance.
\
This clickdata can be represented as a folksonomy
\
in which queries are descriptions of
\
clicked URLs. The resulting network structure,
\
which we will term logsonomy is very
\
similar to the one of folksonomies. In order
\
to find out about its properties, we analyze
\
the topological characteristics of the tripartite
\
hypergraph of queries, users and bookmarks
\
on a large snapshot of del.icio.us and
\
on query logs of two large search engines.
\
All of the three datasets show small world
\
properties. The tagging behavior of users,
\
which is explained by preferential attachment
\
of the tags in social bookmark systems, is
\
reflected in the distribution of single query
\
words in search engines. We can conclude
\
that the clicking behaviour of search engine
\
users based on the displayed search results
\
and the tagging behaviour of social bookmarking
\
users is driven by similar dynamics.	c79a83ec3af1e440d6c2933a50853a13	6d34ea1823d95b9dbf37d4db4d125d2a	e64d14f3207766f4afc65983fa759ffe	inproceedings	Logsonomy - Social Information Retrieval with Logdata	Beate Krause and Robert Jäschke and Andreas Hotho and Gerd Stumme	\N	2008
8133201	\N	\N	\N	\N	\N	\N	SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval	\N	\N	\N	ACM	New York, NY, USA	\N	\N	290954	http://portal.acm.org/citation.cfm?id=290954	\N	On-line new event detection and tracking	\N	\N	37--45	\N	\N	\N	location = {Melbourne, Australia}, isbn = {1-58113-015-5}, doi = {http://doi.acm.org/10.1145/290941.290954}	\N	a13cea5f88c3c6d3378dfafc75b1d8f7	6d9ab08078d7891e60236de5a7373ab8	3927621e8455456ad13373fa0f1e763d	inproceedings	On-line new event detection and tracking	James Allan and Ron Papka and Victor Lavrenko	\N	1998
8212374	\N	\N	\N	\N	May	\N	\N	\N	\N	\N	The MIT Press	Cambridge, MA ; London	\N	\N	wordnet	http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8106	\N		\N	\N	\N	\N	\N	\N	isbn = {978-0-262-06197-1}	WordNet, an electronic lexical database, is considered to be the most important resource available to researchers in computational linguistics, text analysis, and many related areas. Its design is inspired by current psycholinguistic and computational theories of human lexical memory. English nouns, verbs, adjectives, and adverbs are organized into synonym sets, each representing one underlying lexicalized concept. Different relations link the synonym sets.
\

\
The purpose of this volume is twofold. First, it discusses the design of WordNet and the theoretical motivations behind it. Second, it provides a survey of representative applications, including word sense identification, information retrieval, selectional preferences of verbs, and lexical chains.	eb97d1310944ac3a416206607e518fe7	42daa1681607dd1d3f3234c605d84ec3	2e282d8bb3aa81b255b179c0772b3bf7	book	WordNet An Electronic Lexical Database 	\N	Christiane Fellbaum	1998
8216182	\N	\N	\N	\N	\N	\N	The Changing Governance of the Sciences: The Advent of Research Evaluation Systems	\N	\N	\N	Springer	Dordrecht	\N	\N	Gläser07	\N	\N		\N	\N	101-123	\N	\N	\N		\N	c51fc1eedf9b086cb7f03dbe1b50a144	28f1de39ec6d448b949792b55244501e	4c443e2956ae452566748e8d767adeee	incollection	The Social Construction of Bibliometric Evaluations	Jochen Gläser and Grit Laudel	Richard Whitley and Jochen Gläser	2007
8217742	\N	\N	\N	\N	\N	\N	WWW '02: Proceedings of the 11th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	511506	http://portal.acm.org/citation.cfm?id=511446.511506	\N	Authoring and annotation of web pages in CREAM	\N	\N	462--473	\N	\N	\N	location = {Honolulu, Hawaii, USA}, isbn = {1-58113-449-5}, doi = {http://doi.acm.org/10.1145/511446.511506}	Richly interlinked, machine-understandable data constitute the basis for the Semantic Web. We provide a framework, CREAM, that allows for creation of metadata. While the annotation mode of CREAM allows to create metadata for existing web pages, the authoring mode lets authors create metadata --- almost for free --- while putting together the content of a page.As a particularity of our framework, CREAM allows to create relational metadata, i.e. metadata that instantiate interrelated definitions of classes in a domain ontology rather than a comparatively rigid template-like schema asm Dublin Core. We discuss some of the requirements one has to meet when developing such an ontology-based framework, e.g. the integration of a metadata crawler, inference services, document management and a meta-ontology, and describe its implementation, viz. Ont-O-Mat, a component-based, ontology-driven Web page authoring and annotation tool.	0a6c5f13f1854df13249a6f2f4d2739d	acd4e749a39e71ac883d03d6fa874479	8758a88d1564f9dc6b9288fad917191b	inproceedings	Authoring and annotation of web pages in CREAM	Siegfried Handschuh and Steffen Staab	\N	2002
8218211	The Semantic Web – ISWC 2005	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	haase2005	http://dx.doi.org/10.1007/11574620_27	\N	SpringerLinkA Framework for Handling Inconsistency in Changing Ontologies	\N	\N	353--367	\N	\N	\N		One of the major problems of large scale, distributed and evolving ontologies is the potential introduction of inconsistencies. In this paper we survey four different approaches to handling inconsistency in DL-based ontologies: consistent ontology evolution, repairing inconsistencies, reasoning in the presence of inconsistencies and multi-version reasoning. We present a common formal basis for all of them, and use this common basis to compare these approaches. We discuss the different requirements for each of these methods, the conditions under which each of them is applicable, the knowledge requirements of the various methods, and the different usage scenarios to which they would apply.
\
ER  -	196b070b36ac416993a559774c860c8b	e5b54cf13381a791d7f18aafc2bda32b	bbeba3af71f3cb6673abeb908ade599a	article	A Framework for Handling Inconsistency in Changing Ontologies	Peter Haase and Frank Harmelen and Zhisheng Huang and Heiner Stuckenschmidt and York Sure	\N	2005
8218259	Journal of Information Science	32	\N	\N	April	\N	\N	\N	\N	\N	\N	\N	\N	\N	Golder06structureCollaborative	http://arxiv.org/abs/cs/0508082	\N	[cs/0508082] The Structure of Collaborative Tagging Systems	\N	cite arxiv:cs/0508082
\
	198–208	\N	2	\N		  Collaborative tagging describes the process by which many users add metadata
\
in the form of keywords to shared content. Recently, collaborative tagging has
\
grown in popularity on the web, on sites that allow users to tag bookmarks,
\
photographs and other content. In this paper we analyze the structure of
\
collaborative tagging systems as well as their dynamical aspects. Specifically,
\
we discovered regularities in user activity, tag frequencies, kinds of tags
\
used, bursts of popularity in bookmarking and a remarkable stability in the
\
relative proportions of tags within a given url. We also present a dynamical
\
model of collaborative tagging that predicts these stable patterns and relates
\
them to imitation and shared knowledge.
\
	63129db05af3a1e2b4a832b14d48636e	03565ad9c6fc315068e528a53ed158ae	7589d4c250a37b83b810427309cda03d	article	The Structure of Collaborative Tagging Systems	Scott Golder and Bernardo A. Huberman	\N	2006
8218322	Motivation and Emotion	25	\N	\N	sep	\N	\N	\N	\N	\N	\N	\N	\N	\N	chulef2001hierarchical	http://dx.doi.org/10.1023/A:1012225223418	\N		\N	\N	191--232	\N	3	\N		This paper presents a hierarchical taxonomy of human goals, based on similarity judgments of 135 goals gleaned from the literature. Women and men in 3 age groups—17–30, 25–62, and 65 and older—sorted the goals into conceptually similar groups. These were cluster analyzed and a taxonomy of 30 goal clusters was developed for each age group separately and for the total sample. The clusters were conceptually meaningful and consistent across the 3 samples. The broadest distinction in each sample was between interpersonal or social goals and intrapersonal or individual goals, with interpersonal goals divided into family-related and more general social goals. Further, the 30 clusters were organized into meaningful higher order clusters. The role of such a taxonomy in promoting theory development and research is discussed, as is its relationship to other organizations of human goals and to the Big Five structure of personality.
\
ER  -	622ae082205134dccc432fc1d6db91e6	542f8b7cb31c6d0387c60b1d20e60047	f029faed2025b2c7ad36da3f7717e4dc	article	A Hierarchical Taxonomy of Human Goals	Ada S. Chulef and Stephen J. Read and David A. Walsh	\N	2001
8218869	CoRR	cmp-lg/9709008	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	jiang97semantic	\N	\N		\N	\N	\N	\N	\N	\N	ee = {http://arxiv.org/abs/cmp-lg/9709008}	\N	062d4354d1bdfcea7930ee5a1cb3abd9	175ec03ee8c47d4b2d0a083609a78e05	9fb00ff398ecc49ee58bfe6ecaaec108	article	Semantic similarity based on corpus statistics and lexical taxonomy	Jay J. Jiang and David W. Conrath	\N	1997
8223496	\N	\N	\N	\N	March	\N	\N	\N	\N	\N	\N	\N	\N	\N	citeulike:155	http://arxiv.org/abs/cond-mat/0303516	\N	CiteULike: My library [124 articles]	\N	\N	\N	\N	\N	\N	posted-at = {2008-12-12 22:16:47}, priority = {2}, citeulike-article-id = {155}, eprint = {cond-mat/0303516}	Inspired by empirical studies of networked systems such as the Internet,\
social networks, and biological networks, researchers have in recent years\
developed a variety of techniques and models to help us understand or predict\
the behavior of these systems. Here we review developments in this field,\
including such concepts as the small-world effect, degree distributions,\
clustering, network correlations, random graph models, models of network growth\
and preferential attachment, and dynamical processes taking place on networks.	831880b307045a27a0de05603cd740ff	7bedd01cb4c06af9f5200b0fb3faa571	d53568209eef08fb0a8734cf34c59a71	misc	The structure and function of complex networks	M. E. J. Newman	\N	2003
8225116	\N	\N	\N	\N	\N	\N	Computational Learing Theory 	\N	\N	\N	\N	\N	\N	\N	boser92svm	http://www.svms.org/training/BOGV92.pdf	\N		\N	\N	144--152 	\N	\N	\N		\N	7eb4ebffddb7c41c978385f9f6389db2	81c1ca02cfdb4006d4ae602fcbbafcd3	7760ca3eab90ebf595d3118336b756b9	inproceedings	A Training Algorithm for Optimal Margin Classifiers	Bernhard E. Boser and Isabelle Guyon and Vladimir Vapnik	\N	1992
8233129	D-Lib Magazine	11	\N	\N	Apr	xx	\N	\N	\N	\N	\N	\N	\N	\N	Lund2005	http://www.dlib.org/dlib/april05/lund/04lund.html	\N	From Connotea	\N	10.1045/april2005-lund	\N	\N	\N	\N	issn = {1082-9873}, issue = {4}	\N	79632644447be4ea9da931633d427f56	46c0a98ab6ccb96ff4722f35781807de	77c031f99164ed63a20a0ecd89784a37	article	Social Bookmarking Tools (II): A Case Study - Connotea	Ben Lund and Tony Hammond and Martin Flack and Timo Hannay	\N	2005
8233130	D-Lib Magazine	11	\N	\N	Apr	xx	\N	\N	\N	\N	\N	\N	\N	\N	Hammond2005	http://www.dlib.org/dlib/april05/hammond/04hammond.html	\N	From Connotea	\N	10.1045/april2005-hammond	\N	\N	\N	\N	issn = {1082-9873}, issue = {4}	\N	afcc2e296e0aef4559af11cde843813d	c7457d9dc07545a061de119d96ca4e47	079328e18d9bf37d9b94c8343bbfee17	article	Social Bookmarking Tools (I): A General Review	Tony Hammond and Timo Hannay and Ben Lund and Joanna Scott	\N	2005
8233557	Philosophical Transactions of the Royal Society, London	209	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	mercer1909theorem	\N	\N		\N	\N	415--446	\N	\N	\N		\N	f8b775c31e0611f1fd6017352dd9ae74	bdc820a36362be8a416e89783780f939	b2b633fd285dcc32da54f551dcb5aa07	article	Functions of positive and negative type, and their connection with the theory of integral equations	James Mercer	\N	1909
8241271	\N	\N	\N	\N	\N	\N	Proceedings of the 40th Anniversary Meeting of the  Association for Computational Linguistics	\N	\N	\N	\N	\N	\N	\N	cunningham2002	\N	\N		\N	\N	\N	\N	\N	\N		\N	096988be22248ea6ed1fd5ec0b1e14a7	83a721f9dbabe6f90da89b849689e872	10e078af8de149761c28ff7d949e7a30	inproceedings	GATE: A framework and graphical development environment for robust NLP tools and applications	H. Cunningham and D. Maynard and K. Bontcheva and V. Tablan	\N	2002
8248982	ACM Comput. Surv.	23	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	journals/csur/Goldberg91	http://dblp.uni-trier.de/db/journals/csur/csur23.html#Goldberg91	\N	dblp	\N	\N	5-48	\N	1	\N	date = {2003-11-19}	\N	7925b514937d2e3c8c9a87ce8b13e75d	e0560f326bbcab725072d57ca093f45f	69ed8a54d3c0c4d166b5be979ef79f1c	article	What Every Computer Scientist Should Know About Floating-Point Arithmetic.	David Goldberg	\N	1991
8252789	\N	\N	18	\N	\N	\N	Social Semantic Web	\N	\N	\N	Springer	Berlin, Heidelberg	\N	X.media.press	hotho2008social	http://dx.doi.org/10.1007/978-3-540-72216-8_18	\N	SpringerLink - Buchkapitel	\N	\N	363--391	\N	\N	\N	issn = {1439-3107}, isbn = {978-3-540-72215-1}, doi = {10.1007/978-3-540-72216-8}	BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung
\
der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der inBibSonomy und ähnlichen Systemen enthaltenen Daten ein.	16b8f07a41097cc4e4fc962eaf809465	79dbca4289cfe913aa7f7eb7e0dccea7	5ccf05a86e7f1a089ae83dd47568e6de	incollection	Social Bookmarking am Beispiel BibSonomy	Andreas Hotho and Robert Jäschke and Dominik Benz and Miranda Grahl and Beate Krause and Christoph Schmitz and Gerd Stumme	Andreas Blumauer and Tassilo Pellegrini	2009
8265458	World Wide Web	1	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	kittur2007pfv	\N	\N		\N	\N	19	\N	2	\N		\N	15e2b059e14877307c257d20ba74ad0f	4ccf92baed65a7c6f19fdc3e8cb38b82	e12b1f58bc9f05e619be121e5623a1c4	article	{Power of the few vs. wisdom of the crowd: Wikipedia and the rise of the bourgeoisie}	A. Kittur and E. Chi and B.A. Pendleton and B. Suh and T. Mytkowicz	\N	2007
8299475	The Semantic Web – ISWC 2005	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	paper:mika:2005	http://dx.doi.org/10.1007/11574620_38	\N	SpringerLink - Book Chapter	\N	\N	522--536	\N	\N	\N		In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.
\
ER  -	64944a9d7d2f9d53396104020d2c2216	5ea12110b5bb0e3a8ad09aeb16a70cdb	12bba91c38e6cdbec4e8a9372d1f3891	article	Ontologies Are Us: A Unified Model of Social Networks and Semantics	Peter Mika	\N	2005
8314822	J. Am. Soc. Inf. Sci. Technol.	57	\N	\N	\N	\N	\N	\N	\N	\N	John Wiley \\& Sons, Inc.	New York, NY, USA	\N	\N	Rose06	\N	\N		\N	\N	797--799	\N	6	\N	issn = {1532-2882}, doi = {http://dx.doi.org/10.1002/asi.v57:6}	\N	caaa5638922333d25f75e9010eb210e7	f1303a1ab9cc1546beeaae69835ef4f5	43c9b39a6a15199210b600b5a57ad67a	article	Reconciling information-seeking behavior with search user interfaces for the Web	Daniel E. Rose	\N	2006
8317923	Queue	3	\N	\N	\N	\N	\N	\N	\N	\N	ACM	New York, NY, USA	\N	\N	millen2005social	http://portal.acm.org/citation.cfm?id=1105676#	\N		\N	\N	28--35	\N	9	\N	issn = {1542-7730}, doi = {http://doi.acm.org/10.1145/1105664.1105676}	One of the greatest challenges facing people who use large information spaces is to remember and retrieve items that they have previously found and thought to be interesting. One approach to this problem is to allow individuals to save particular search strings to re-create the search in the future. Another approach has been to allow people to create personal collections of material—for example, the use of electronic citation bundles (called binders) in the ACM Digital Library. Collections of citations can be created manually by readers or through execution of (and alerting to) a saved search. 	09be98f66856455d93602b9274445604	b40410a542f48202c52b6fa9408bca79	dbc6366c82bbdb25c9865083b528f748	article	Social Bookmarking in the Enterprise	David Millen and Jonathan Feinberg and Bernard Kerr	\N	2005
8318706	\N	\N	\N	\N	February	\N	\N	Blog post	\N	\N	\N	\N	\N	\N	vander2007explaining	http://www.personalinfocloud.com/2005/02/explaining_and_.html	\N		\N	\N	\N	\N	\N	\N		I have been explaining the broad and narrow folksonomy in e-mail and in comments on others sites, as well as in the media (Wired News). There has still been some confusion, which is very understandable as it is a different concept that goes beyond a simple understanding of tagging. I have put together a couple graphics that should help provide a means to make this distinction some what clearer. The folksonomy is a means for people to tag objects (web pages, photos, videos, podcasts, etc., essentially anything that is internet addressable) using their own vocabulary so that it is easy for them to refind that information again. The folksonomy is most often also social so that others that use the same vocabulary will be able to find the object as well. It is important to note that folksonomies work best when the tags used to describe objects are in the common vocabulary and not what a person perceives others will call it (the tool works like no other for personal information management of information on the web, but is also shared with the world to help others find the information).	59606ade43c09ebbb14bbc3339bb74dd	66814778a23c347a0d24a484b3f6bd7d	3c3f33e5727395fdade9212f9cb6e242	misc	Explaining and Showing Broad and Narrow Folksonomies	Thomas Vander Wal	\N	2005
8324451	\N	3999	\N	\N	\N	\N	Natural Language Processing and Information Systems	\N	\N	\N	Springer	Berlin/Heidelberg	\N	Lecture Notes in Computer Science	veres2006language	http://dx.doi.org/10.1007/11765448_6	\N		\N	\N	58--69	\N	\N	\N	issn = {0302-9743}, isbn = {978-3-540-34616-6}, doi = {10.1007/11765448}	Folksonomies are classification schemes that emerge from the collective actions of users who tag resources with an unrestricted
\
set of key terms. There has been a flurry of activity in this domain recently with a number of high profile web sites andsearch engines adopting the practice. They have sparked a great deal of excitement and debate in the popular and technicalliterature, accompanied by a number of analyses of the statistical properties of tagging behavior. However, none has addressedthe deep nature of folksonomies. What is the nature of a tag? Where does it come from? How is it related to a resource? Inthis paper we present a study in which the linguistic properties of folksonomies reveal them to contain, on the one hand,tags that are similar to standard categories in taxonomies. But on the other hand, they contain additional tags to describeclass properties. The implications of the findings for the relationship between folksonomy and ontology are discussed.	a182102e37f24170475d7c74f6f741b6	1787dec43f3c11153fc9d2617af8829c	d0e5be1774a6094049df3e6d604f1957	inproceedings	The Language of Folksonomies: What Tags Reveal About User Classification	Csaba Veres	Christian Kop and Günther Fliedl and Heinrich C. Mayr and Elisabeth Métais	2006
8326119	\N	\N	\N	\N	sep	\N	Proceedings of the 6th International Conference on Knowledge Management (I-KNOW 06)	\N	\N	\N	\N	Graz, Austria	\N	\N	bloehdorn2006tagfs	http://semfs.ontoware.org/pubs/2006-09-iknow2006-tagfs.pdf	\N		\N	\N	\N	\N	\N	\N		Today, most computer users work with traditional hierarchical file systems for organizing large amounts of personal files. Recently, tagging has grown popular as an alternative means of organizing information resources. We argue that tagging is a powerful paradigm for efficient information access which overcomes many deficiencies of hierarchical file systems, especially in the context of the organization of large quantities of personal files. In this paper we present TagFS, a filesystem with tagging support which aims at a seamless integration of the tagging paradigm with local applications. While retaining the notions of directories and files and providing all standard filesystem operations, the semantics of these primitives are changed to modifications of the tag annotations.	423972cded05e0249fb2183f6fc72af7	baad177aa0bf9293b58958039e38f526	1adbbf3e0ddd26e4b1df2eab90557f64	inproceedings	TagFS - Tag Semantics for Hierarchical File Systems	Stephan Bloehdorn and Olaf Görlitz and Simon Schenk and Max Völkel	\N	2006
8332591	\N	\N	\N	\N	\N	\N	Proc. of 2nd International Conference on Knowledge Discovery and  Data Mining (KDD-96)	\N	\N	\N	\N	\N	\N	\N	Ester1996	\N	\N		\N	\N	226-231	\N	\N	\N	file = {:KDD96-037.pdf:PDF}	\N	eb7f6426c1536830f5c2180db7099d3d	ba33e4d6b4e5b26bd9f543f26b7d250a	2f9e50f0a003c4d3067cab2b6fa47fe0	inproceedings	A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise	Martin Ester and Hans-Peter Kriegel and J{\\"o}rg Sander and Xiaowei Xu	\N	1996
8334489	Proceedings of the WWW 2008 Workshop Linked Data on the Web (LDOW2008), Beijing, China, Apr	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	PL08	http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-369/paper22.pdf	\N		\N	\N	\N	\N	\N	\N	posted-at = {2009-03-31 00:02:34}, priority = {4}, citeulike-article-id = {3172586}	This paper introduces MOAT, a lightweight Semantic Web framework that provides a collaborative way to let Web 2.0 content producers give meanings to their tags in a machinereadable way. To achieve this goal, this approach relies on Linked Data principles, using URIs from existing resources to define these meanings. That way, users can create interlinked RDF data and let their content enter the Semantic Web, while solving some limits of free-tagging at the same time.	6ce9b22f1c54def9db37b3dc3f28257f	c6ef7c21e091847e34368730e29a6b94	9aa3eaabb7327971abeb82ac0d7a348d	article	Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data	A. Passant and P. Laublet	\N	2008
8334516	\N	\N	\N	\N	November	\N	The International Workshop on Emergent Semantics and Ontology Evolution (ESOE2007) at ISWC/ASWC 2007	\N	\N	\N	\N	\N	\N	\N	citeulike:3000295	http://eprints.ecs.soton.ac.uk/14869/	\N		\N	\N	108--121	\N	\N	\N	posted-at = {2008-07-14 23:05:10}, citeulike-article-id = {3000295}, priority = {5}	The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users' own way. In this paper, we present a possible way to analyze the tripartite graphs - graphs involving users, tags and resources - of folksonomies and discuss how these elements acquire their meanings through their associations with other elements, a process we call mutual contextualization. In particular, we demonstrate how different meanings of ambiguous tags can be discovered through such analysis of the tripartite graph by studying the tag sf. We also discuss how the result can be used as a basis to better understand the nature of folksonomies.	ddd2150ceeb88322a64f1daa485d9a1d	8d1bea2571673b0b9cdb818043a3a7db	9523ce1f2afbe0dcfd5ad000531406b7	inproceedings	Understanding the Semantics of Ambiguous Tags in Folksonomies	{Ching Man Au} Yeung and Nicolas Gibbins and Nigel Shadbolt	\N	2007
8334763	\N	\N	\N	1	November	\N	\N	\N	\N	\N	Cambridge University Press	\N	\N	\N	Wasserman1994	\N	\N		\N	\N	\N	\N	\N	\N	isbn = {0521387078}	\N	770832a9405bfec99c8abb5f95dd1ace	387e48dafbb99962c628d30bfe9aa527	8c66c9e666572c9d11e6d8124c7ba567	book	Social Network Analysis: Methods and Applications	Stanley Wasserman and Katherine Faust	\N	1994
8334814	\N	\N	\N	\N	\N	\N	The Semantic Web. Research and Applications. Proceedings of the 4 th European Semantic Web Conference	\N	\N	\N	Springer Verlag	\N	\N	\N	ieKey	ftp://ftp.inrialpes.fr/pub/exmo/publications/jung2007a.pdf	\N		\N	\N	\N	\N	\N	\N	location = {New York:}, date = {(2007)}	\N	c6d9cac28d71e457d1560c4a56d4a1e8	0bc892e8be19f20c3ba23ed32ed23cff	426f17fd80438cf533e50cd1838b4d1e	misc	Towards Semantic Social Networks	Jason J. Jung and Jerome Euzenat	Enrico Franconi and Michael Kifer and Wolfgang May	2007
8370198	IEEE Transactions on Software Engineering	20	\N	\N	June	\N	\N	\N	\N	\N	\N	\N	\N	\N	Chidamber1994	\N	\N	\N	\N	\N	476-493	\N	6	\N		Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a new suite of metrics for OO design. Metrics developed in previous research, while contributing to the field's understanding of software development processes, have generally been subject to serious criticisms, including the lack of a theoretical base. Following Wand and Weber (1989), the theoretical base chosen for the metrics was the ontology of Bunge (1977). Six design metrics are developed, and then analytically evaluated against Weyuker's (1988) proposed set of measurement principles. An automated data collection tool was then developed and implemented to collect an empirical sample of these metrics at two field sites in order to demonstrate their feasibility and suggest ways in which managers may use these metrics for process improvement	b86a64ba80bdc43bc1a0909888517bba	b9682f70d3f17b9225ceddf8dc993764	bd1c8652d06877f62f374b5466f01953	article	A Metrics Suite for Object Oriented Design	Shyam R. Chidamber and Chris F. Kemerer	\N	1994
8370795	IEEE Transactions on Software Engineering	22	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Basili1996	\N	\N	\N	\N	\N	751-761	\N	10	\N		\N	0281923f8d1ece0b6910502ede86c5c1	1b4e625b6ca10dd395ccda9313138555	723e8b282d2aec1f4907205710948e31	article	A Validation of Object-Oriented Design Metrics as Quality Indicators	Victor R. Basili and Lionel C. Briand and Walcelio L. Melo	\N	1996
8378140	\N	\N	\N	\N	apr	\N	AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	krause2008anti	http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf	\N		\N	\N	61--68	\N	\N	\N	location = {Beijing, China}, isbn = {978-1-60558-159-0}, doi = {10.1145/1451983.1451998}	The annotation of web sites in social bookmarking systems
\
has become a popular way to manage and find information
\
on the web. The community structure of such systems attracts
\
spammers: recent post pages, popular pages or specific
\
tag pages can be manipulated easily. As a result, searching
\
or tracking recent posts does not deliver quality results
\
annotated in the community, but rather unsolicited, often
\
commercial, web sites. To retain the benefits of sharing
\
one’s web content, spam-fighting mechanisms that can face
\
the flexible strategies of spammers need to be developed.	a10680818f39bd49145e327362627931	a45d40ac7776551301ad9dde5b25357f	5b6b648fd25c15d594404ae26fcda6b4	inproceedings	The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems	Beate Krause and Christoph Schmitz and Andreas Hotho and Gerd Stumme	\N	2008
8415780	\N	\N	\N	\N	November	\N	In: Proc. of ISWC-2006 International Semantic Web Conference	\N	\N	\N	Springer, LNCS	Athens, GA, USA	\N	\N	dellschaft2006GoldEvalOntoLearn	http://iswc2006.semanticweb.org/items/paper_44.php	\N	On How to Perform a Gold Standard based Evaluation of Ontology Learning	\N	\N	\N	\N	\N	\N	pdf = {http://www.uni-koblenz.de/~staab/Research/Publications/2006/DellschaftStaabISWCsubmitted.pdf}	\N	8283ee9b71863739a2265a16fe132f21	bd5dcdc47711f5dce1a2546db5b66e79	0bfd502e363ef3f1523d77f972f08397	inproceedings	On How to Perform a Gold Standard based Evaluation of Ontology Learning	Klaas Dellschaft and Steffen Staab	\N	2006
8416059	\N	\N	\N	\N	\N	\N	GROUP '07: Proceedings of the 2007 international ACM conference on Conference on supporting group work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	1316677	http://portal.acm.org/citation.cfm?id=1316677&coll=Portal&dl=GUIDE&CFID=9767993&CFTOKEN=86305662	\N	Evaluating tagging behavior in social bookmarking systems	\N	\N	351--360	\N	\N	\N	location = {Sanibel Island, Florida, USA}, isbn = {978-1-59593-845-9}, doi = {http://doi.acm.org/10.1145/1316624.1316677}	\N	2548151c2ff18a8169f9519284c46834	66928ca91bf0d777b848fe6f7a55de20	5d0b61727d81aed019ba4297090108ca	inproceedings	Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics	Umer Farooq and Thomas G. Kannampallil and Yang Song and Craig H. Ganoe and John M. Carroll and Lee Giles	\N	2007
8424656	\N	\N	\N	\N	\N	\N	CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work	\N	\N	\N	ACM	New York, NY, USA	\N	\N	sen2006	http://portal.acm.org/citation.cfm?id=1180904	\N	tagging, communities, vocabulary, evolution	\N	\N	181--190	\N	\N	\N	location = {Banff, Alberta, Canada}, isbn = {1-59593-249-6}, doi = {http://doi.acm.org/10.1145/1180875.1180904}	A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.	cfb9aaa70229cd3e4a34c42fa56d8f42	96b20bffcbc91e528461529935524b90	e66c4381674cc0d9691c455254da0225	inproceedings	tagging, communities, vocabulary, evolution	S. Sen and S. K. Lam and A. Mamunur Rashid and D. Cosley and D. Frankowski and J. Osterhouse and F. Maxwell Harper and J. Riedl	\N	2006
8442352	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	O'Reilly	Sebastopol, CA, USA	\N	\N	RichardsonRuby07	\N	\N	Updated base references	\N	\N	\N	\N	\N	\N	timestamp = {2008.05.01}, isbn = {0-596-52926-0}, owner = {flint}	This book emphasizes the power of basic Web technologies -- the HTTP\
\	application protocol, the URI naming standard, and the XML markup\
\	language; introduces the Resource-Oriented Architecture (ROA), a\
\	common-sense set of rules for designing RESTful web services; Shows\
\	how a RESTful design is simpler, more versatile, and more scalable\
\	than a design based on Remote Procedure Calls (RPC); and includes\
\	real-world examples of RESTful web services, like Amazon's Simple\
\	Storage Service and the Atom Publishing Protocol; discusses web service\
\	clients for popular programming languages. It also shows you how\
\	to implement RESTful services in three popular frameworks - Ruby\
\	on Rails, Restlet (for Java), and Django (for Python), and focuses\
\	on practical issues such as: how to design and implement RESTful\
\	web services and clients. This is the first book that applies the\
\	REST design philosophy to real web services. It sets down the best\
\	practices you need to make your design a success, and the techniques\
\	you need to turn your design into working code. You can harness the\
\	power of the Web for programmable applications: you just have to\
\	work with the Web instead of against it.	02a9e33ecb072389b228fad61b66ec8e	990343ac0d056dad54cf06e7c6a1d2e1	a331d881118c83e0a31d45159f5f5850	book	RESTful Web Services	Leonard Richardson and Sam Ruby	\N	2007
8453990	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Springer	\N	\N	\N	cig01	\N	\N		\N	\N	\N	\N	\N	\N	callnumber = {794.8151 COM}	\N	e715c41fc4aa138ba12943af856e3992	571ecc98c834f6c622dc7e059ba5798c	c22dbe72b3dcedf40536cc69bf0e2e5c	book	Computational Intelligence in Games	\N	Norio Baba and Lakhmi C. Jain	2001
8492664	\N	\N	\N	\N	\N	\N	HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia	\N	\N	\N	ACM	New York, NY, USA	\N	\N	Chi2008	http://portal.acm.org/citation.cfm?id=1379110	\N	Understanding the efficiency of social tagging systems using information theory	\N	\N	81--88	\N	\N	\N	location = {Pittsburgh, PA, USA}, isbn = {978-1-59593-985-2}, doi = {http://doi.acm.org/10.1145/1379092.1379110}	\N	70d49861635a3e467ccdccec87043b14	81c80283290d396a41015d0df11822c7	dfa880e6d3e33d0aeb357396fb1833cd	inproceedings	Understanding the efficiency of social tagging systems using information theory	Ed H. Chi and Todd Mytkowicz	\N	2008
8506737	Physical Review Letters	89	\N	\N	oct	\N	\N	\N	\N	\N	American Physical Society	\N	\N	\N	newman02assortative	http://link.aps.org/doi/10.1103/PhysRevLett.89.208701	\N		\N	\N	208701	\N	20	\N	doi = {10.1103/PhysRevLett.89.208701}	A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.	a48e6be8c07ff7140ae4b8d06cf6a33b	7265c6dc287861591f52e46b17404a08	3ba2913f29e817d122b41e8d78aeeecf	article	Assortative Mixing in Networks	M. E. J. Newman	\N	2002
8506868	CoRR	abs/0704.3316	\N	\N	apr	\N	\N	\N	\N	\N	\N	\N	\N	\N	cattuto2007growth	http://arxiv.org/abs/0704.3316	\N	dblp	\N	arXiv:0704.3316v1	\N	\N	\N	\N		We analyze a large-scale snapshot of del.icio.us and investigate how the number of different tags in the system grows as a function of a suitably defined notion of time. We study the temporal evolution of the global vocabulary size, i.e. the number of distinct tags in the entire system, as well as the evolution of local vocabularies, that is the growth of the number of distinct tags used in the context of a given resource or user. In both cases, we find power-law behaviors with exponents smaller than one. Surprisingly, the observed growth behaviors are remarkably regular throughout the entire history of the system and across very different resources being bookmarked. Similar sub-linear laws of growth have been observed in written text, and this qualitative universality calls for an explanation and points in the direction of non-trivial cognitive processes in the complex interaction patterns characterizing collaborative tagging. 	a776604727d9703509e98397efa80036	7de017393b2d48335e209a9db23e08b6	04bc17658d8d028e01d69123b5dc6b40	article	Vocabulary growth in collaborative tagging systems	Ciro Cattuto and Andrea Baldassarri and Vito Domenico Pietro Servedio and Vittorio Loreto	\N	2007
8506913	CoRR	abs/cs/0605015	\N	\N	may	\N	\N	\N	\N	\N	\N	\N	\N	\N	cattuto2006collaborative	http://arxiv.org/abs/cs/0605015	\N	dblp	\N	arXiv:cs/0605015v1	\N	\N	\N	\N	doi = {10.1073/pnas.0610487104}	Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag co-occurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory - or aging of resources - in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features, with a surprisingly high accuracy. This points in the direction of a universal behavior of users, who - despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity - appear to follow simple activity patterns. 	894c4ad6552adad62fa5517c350bd295	59b1bd0ed96f41d2c3c98ff232df5dd2	f07137d804ee7b6809a71bd1f832dc2a	article	Collaborative Tagging and Semiotic Dynamics	Ciro Cattuto and Vittorio Loreto and Luciano Pietronero	\N	2006
8507186	\N	209	\N	\N	nov	\N	Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06)	\N	\N	\N	\N	\N	\N	CEUR-WS.org	halpin2006dynamics	http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-209/saaw06-full01-halpin.pdf	\N	CEUR-WS.org/Vol-209 - SAAW2006 - Semantic Authoring and Annotation Workshop	\N	\N	\N	\N	\N	\N	issn = {1613-0073}	The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classi- fication schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from “popular” tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.	9f5938b8335051affbfe8d2f647500c4	86b08d03b5f0bd947fd9095dc2c9a70c	1fb8cd3ba20e70453b02e2213b9a016f	inproceedings	The Dynamics and Semantics of Collaborative Tagging 	Harry Halpin and Valentin Robu and Hana Shepard	Knud Möller and Anita de Waard and Steve Cayzer and Marja-Riitta Koivunen and Michael Sintek and Siegfried Handschuh	2006
8509275	\N	202	\N	\N	nov	\N	Proceedings of the Semantic Desktop and Social Semantic Collaboration Workshop (SemDesk 2006) at the 5th International Semantic Web Conference ISWC 2006	\N	\N	\N	\N	\N	\N	CEUR-WS.org	brunkhorst2006beagle	http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-202/SEMDESK2006_0028.pdf	\N		\N	\N	\N	\N	\N	\N	issn = {1613-0073}	The rapidly increasing quantity and diversity of data stored on our PCs made locating information in this environment very difficult. Consequently, recent research has focussed on building semantically enhanced systems for either organizing or searching data on the desktop. Building on previous work, in this paper we present the Beagle++ toolbox, a set of extendable building blocks for implementing such a system. The corresponding modular desktop search architecture integrates our previously developed metadata generators and ranking components, uses an RDF database to share data between components, and can easily integrate other external components to improve desktop search quality. Additionally, we provide implementation details about all our current components, how they interact with each other, and how to install the complete system on top of a Linux distribution.	37be22bc62bc6f88990abbfa0b8fb75b	0e643a96576d17cbdb49c2c8210c40dd	22e23038e827d350db073cebc7ba9133	inproceedings	The Beagle++ Toolbox: Towards an Extendable Desktop Search Architecture	Ingo Brunkhorst and Paul Alexandru Chirita and Stefania Costache and Julien Gaugaz and Ekaterini Ioannou and Tereza Iofciu and Enrico Minack and Wolfgang Nejdl and Raluca Paiu	Stefan Decker and Jack Park and Leo Sauermann and Sören Auer and Siegfried Handschuh	2006
8532063	\N	\N	\N	\N	\N	\N	WWW '06: Proceedings of the 15th international conference on World Wide Web	\N	\N	\N	ACM	New York, NY, USA	\N	\N	VKVHS06	http://portal.acm.org/citation.cfm?id=1135863	\N	Semantic Wikipedia	\N	\N	585-594	\N	\N	\N	location = {Edinburgh, Scotland}, isbn = {1-59593-323-9}, doi = {http://doi.acm.org/10.1145/1135777.1135863}	Wikipedia is the world's largest collaboratively edited source of encyclopaedic knowledge. But in spite of its utility, its contents are barely machine-interpretable. Structural knowledge, e.,g. about how concepts are interrelated, can neither be formally stated nor automatically processed. Also the wealth of numerical data is only available as plain text and thus can not be processed by its actual meaning.We provide an extension to be integrated in Wikipedia, that allows the typing of links between articles and the specification of typed data inside the articles in an easy-to-use manner.Enabling even casual users to participate in the creation of an open semantic knowledge base, Wikipedia has the chance to become a resource of semantic statements, hitherto unknown regarding size, scope, openness, and internationalisation. These semantic enhancements bring to Wikipedia benefits of today's semantic technologies: more specific ways of searching and browsing. Also, the RDF export, that gives direct access to the formalised knowledge, opens Wikipedia up to a wide range of external applications, that will be able to use it as a background knowledge base.In this paper, we present the design, implementation, and possible uses of this extension.	084060ea6f24598c91ed7a7da1aca168	7420d68939b85287f3e21692ea61dfdd	e0d87764409eb6c7fd52dcc33de6b189	inproceedings	Semantic Wikipedia	Max Völkel and Markus Krötzsch and Denny Vrandecic and Heiko Haller and Rudi Studer	\N	2006
8581294	\N	\N	\N	1	\N	\N	\N	\N	\N	\N	O'Reilly Media	\N	\N	\N	0596007124	http://www.amazon.de/Head-First-Design-Patterns-Freeman/dp/0596007124%3FSubscriptionId%3D13CT5CVB80YFWJEPWS02%26tag%3Dws%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0596007124	\N	Head First Design Patterns: Amazon.de: Eric Freeman, Elisabeth Freeman, Bert Bates, Kathy Sierra, Mike Loukides: Englische Bücher	\N	\N	\N	\N	\N	\N	ean = {9780596007126}, asin = {0596007124}, isbn = {0596007124}, dewey = {005.1}	\N	bd1f7c0c7a9f570f9eecf50bd77f417d	cae6c7a4082986ff0c492d552844f018	5af893bd4d7bb9111aee3d27f4e03d28	book	Head First Design Patterns	Eric Freeman and Elisabeth Freeman and Bert Bates and Kathy Sierra	Mike Loukides	2004
8593172	\N	\N	\N	\N	may	\N	\N	\N	DERI Galway	\N	\N	Galway, Ireland	\N	\N	decker2004social	http://www.deri.ie/fileadmin/documents/DERI-TR-2004-05-02.pdf	\N		\N	\N	\N	\N	DERI-TR-2004-05-02	\N		This whitepaper we vision of a new group collaboration infrastructure, the Social Semantic Desktop, drawing from co-evolving research in the Semantic Web, Peer-to-Peer (P2P) Networks, and Online Social Networking. The Social Semantic Desktop is a novel collaboration environment, enabling the creation, sharing and deployment of data and metadata.	3adbd4e5805e8e4b8ab3e4da722fdb5e	4b6cde4d39a8c415a85a92508aba600a	937b321b1e6ba9a26c7bcce8872de91c	techreport	The Social Semantic Desktop	Stefan Decker and Martin Frank	\N	2004
8593509	Scientific American	284	\N	\N	may	\N	\N	\N	\N	\N	\N	\N	\N	\N	lee2001semantic	http://www.scientificamerican.com/article.cfm?id=the-semantic-web	\N	dret'd bibliography	\N	\N	34--43	\N	5	\N	issn = {0036-8733}	A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities	fa181876ddd17709de68645eaec33bf6	e87f09446138a81e6478625da97885b6	bef8ed4bf6cf5c1f3ba62f8ab6cc1473	article	The Semantic Web	Tim Berners-Lee and James A. Hendler and Ora Lassila	\N	2001
8608023	\N	\N	\N	\N	\N	\N	WWW '06: Proceedings of the 15th international conference on World Wide Web	\N	\N	\N	ACM Press	New York, NY, USA	\N	\N	1135858	http://portal.acm.org/citation.cfm?id=1135858	\N	Time-dependent semantic similarity measure of queries using historical click-through data	\N	\N	543--552	\N	\N	\N	location = {Edinburgh, Scotland}, isbn = {1-59593-323-9}, doi = {http://doi.acm.org/10.1145/1135777.1135858}	It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines, which record the interactions between users and the search engines. Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor, while the click-through data is often dynamic and contains rich temporal information. In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data. The intuition is that more accurate semantic similarity values between queries can be obtained by taking into account the timestamps of the log data. With a set of user-defined calendar schema and calendar patterns, our time-dependent query similarity model is constructed using the marginalized kernel technique, which can exploit both explicit similarity and implicit semantics from the click-through data effectively. Experimental results on a large set of click-through data acquired from a commercial search engine show that our time-dependent query similarity model is more accurate than the existing approaches. Moreover, we observe that our time-dependent query similarity model can, to some extent, reflect real-world semantics such as real-world events that are happening over time.	d3a4580f34ff8e9418a4706fc1551471	c765e101c37f6b530e2c1c59808048d7	57cbc64550d3a1b5b8599a0783e95111	inproceedings	Time-dependent semantic similarity measure of queries using historical click-through data	Qiankun Zhao and Steven C. H. Hoi and Tie-Yan Liu and Sourav S. Bhowmick and Michael R. Lyu and Wei-Ying Ma	\N	2006
8608030	\N	\N	\N	\N	\N	\N	KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining	\N	\N	\N	ACM	New York, NY, USA	\N	\N	1281204	http://portal.acm.org/citation.cfm?id=1281204	\N	Extracting semantic relations from query logs	\N	\N	76--85	\N	\N	\N	location = {San Jose, California, USA}, isbn = {978-1-59593-609-7}, doi = {http://doi.acm.org/10.1145/1281192.1281204}	\N	654af8d637bc014277cf3e605e2fffde	26ca034be705abaf072835784f53d877	6e45b65feffd1545c6dca62bf4b8f53d	inproceedings	Extracting semantic relations from query logs	Ricardo Baeza-Yates and Alessandro Tiberi	\N	2007
8624063	Nature	438	\N	\N	#dec#	\N	\N	\N	\N	\N	\N	\N	\N	\N	Giles2005	http://dx.doi.org/10.1038/438900a	\N	Internet encyclopaedias go head to head : Article : Nature	\N	\N	900--901	\N	7070	\N	issn = {0028-0836}, comment = {10.1038/438900a}	\N	af77b35bdad9ab7b6355d9a2c8ea8e14	973b0ee047743fedcb3cd7bdd3bf407a	c3c8afbfa50119e30a3a90af8a68a1aa	article	Internet encyclopaedias go head to head	Jim Giles	\N	2005
8625101	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Penguin Press HC, The	\N	\N	\N	1594201536	http://www.shirky.com/herecomeseverybody/	\N	Here Comes Everybody: The Power of Organizing Without Organizations: Clay Shirky: Books	\N	\N	\N	\N	\N	\N	ean = {9781594201530}, asin = {1594201536}, isbn = {1594201536}, dewey = {303.4833}	\N	bb1970a40b9fbabce0214bb87f69f3ba	7a388c01b1f575f6cd338fcedbc177fa	9b53d7fd371630eb9ab71d3ed646230c	book	Here Comes Everybody: The Power of Organizing Without Organizations	Clay Shirky	\N	2008
8651793	\N	1540	\N	\N	\N	\N	ICDT	\N	\N	\N	Springer	\N	\N	Lecture Notes in Computer Science	conf/icdt/BeyerGRS99	http://dblp.uni-trier.de/db/conf/icdt/icdt99.html#BeyerGRS99	\N	dblp	\N	\N	217-235	\N	\N	conf/icdt/99	date = {2002-01-03}, cite = {conf/icde/WhiteJ96}, ee = {http://link.springer.de/link/service/series/0558/bibs/1540/15400217.htm}, isbn = {3-540-65452-6}	\N	a54b3f2f8211196cce126efd4713d70c	17f2a2126af823b1b135231d1c189e7d	b0beff3a9fa219560f51295a27a3fc5a	inproceedings	When Is ''Nearest Neighbor'' Meaningful?	Kevin S. Beyer and Jonathan Goldstein and Raghu Ramakrishnan and Uri Shaft	Catriel Beeri and Peter Buneman	1999
8674094	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Oxford University Press. 	\N	\N	\N	knowledge_creating	\N	\N		\N	\N	\N	\N	\N	\N		\N	5da042f7edcf18d84f4c3c7e6258e22a	8b16d8525a210ad1b798db4632ab0d65	77e959d8efd590fd84836377270aba9a	book	The Knowledge-Creating Company.	I. Nonaka and H. Takeuchi	\N	1995
8675865	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N	Addison-Wesley	\N	\N	The Addison-Wesley Signature Series	Bec03	\N	\N		\N	\N	\N	\N	\N	\N		\N	d6067cedc19a1a3b2ca8ca337e2c6b0f	74cd7efe7bc66d6ca18165c76d848a55	25a30fddea8f29220e9028679dfc295a	book	Test-Driven Development: By Example	Kent Beck	\N	2003
8686739	\N	\N	\N	\N	\N	\N	CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems	\N	\N	\N	ACM	New York, NY, USA	\N	\N	ames2007why	http://portal.acm.org/citation.cfm?doid=1240624.1240772	\N		\N	\N	971--980	\N	\N	\N	location = {San Jose, California, USA}, isbn = {978-1-59593-593-9}, doi = {10.1145/1240624.1240772}	Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture.
\

\
A qualitative study of ZoneTag/Flickr users exposed various tagging patterns and emerging motivations for photo annotation. We offer a taxonomy of motivations for annotation in this system along two dimensions (sociality and function), and explore the various factors that people consider when tagging their photos. Our findings suggest implications for the design of digital photo organization and sharing applications, as well as other applications that incorporate user-based annotation.	3a3566a87c9030963e92fffc660584e1	bd24c17d66d2b904b3fc9444c2b64b44	a65cefd93890f99f0119da73ba18d392	inproceedings	Why we tag: motivations for annotation in mobile and online media	Morgan Ames and Mor Naaman	\N	2007
8689434	\N	\N	\N	\N	\N	\N	SIGMOD '98: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data	\N	\N	\N	ACM Press	\N	\N	\N	agrawal98clique	http://doi.acm.org/10.1145/276304.276314	\N	\N	\N	\N	94-105	\N	\N	sigmod98	file = {agrawal98clique.pdf:papers\\\\agrawal98clique.pdf:PDF}, language = {english}, doi = {http://doi.acm.org/10.1145/276304.276314}, misc = {bibsource = DBLP, http://dblp.uni-trier.de}	\N	dd9540fba9038c68f9e61b1a9e635036	217be1ee3a56834b9dd15206d724f51a	ce0db5f280dfda6e3f6fea5293ad8eb9	inproceedings	Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications	Rakesh Agrawal and Johannes Gehrke and Dimitrios Gunopulos and Prabhakar Raghavan	Laura Haas and Pamela Drew and Ashutosh Tiwary and Michael Franklin	1998
8754105	\N	\N	18	\N	\N	\N	Social Semantic Web	\N	\N	\N	Springer	Berlin, Heidelberg	\N	X.media.press	hotho2008social	http://dx.doi.org/10.1007/978-3-540-72216-8_18	\N	SpringerLink - Buchkapitel	\N	\N	363--391	\N	\N	\N	issn = {1439-3107}, isbn = {978-3-540-72215-1}, doi = {10.1007/978-3-540-72216-8}	BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung
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der Universit{\\"a}t Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalit{\\"a}t, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.	16b8f07a41097cc4e4fc962eaf809465	79dbca4289cfe913aa7f7eb7e0dccea7	5ccf05a86e7f1a089ae83dd47568e6de	incollection	Social Bookmarking am Beispiel BibSonomy	Andreas Hotho and Robert Jäschke and Dominik Benz and Miranda Grahl and Beate Krause and Christoph Schmitz and Gerd Stumme	Andreas Blumauer and Tassilo Pellegrini	2009
