{"id":286,"date":"2016-03-15T16:08:27","date_gmt":"2016-03-15T15:08:27","guid":{"rendered":"https:\/\/www.kde.cs.uni-kassel.de\/?page_id=286"},"modified":"2018-12-15T12:35:50","modified_gmt":"2018-12-15T11:35:50","slug":"mitzlaff","status":"publish","type":"page","link":"https:\/\/www.kde.cs.uni-kassel.de\/en\/mitzlaff","title":{"rendered":"Dr. Folke Mitzlaff"},"content":{"rendered":"<div id=\"trailimageid\"><img decoding=\"async\" id=\"ttimg\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/loading.gif\"><\/div> <p>University of Kassel<img decoding=\"async\" id=\"me\" class=\"alignright\" src=\"http:\/\/www.kde.cs.uni-kassel.de\/mitzlaff\/img\/portrait.jpg\" alt=\"Portrait\" \/><br \/>\nFB 16 Electrical Engineering, Computer Science<br \/>\nWilhelmsh\u00f6her Allee 73<br \/>\n34121 Kassel<\/p>\n<p>Mail: <a href=\"mailto:mitzlaff@cs.uni-kassel.de\"><u><span style=\"color: #0066cc;\">mitzlaff@cs.uni-kassel.de<\/span><\/u><\/a><noscript><\/p>\n<pre>mitzlaff at cs.uni-kassel.de<\/pre>\n<p><\/noscript><br \/>\nTel.: <a href=\"tel:+495618046250\"><u><span style=\"color: #0066cc;\">+49 561 804-6250<\/span><\/u><\/a><br \/>\nFax: <a href=\"fax:+495618046259\"><u><span style=\"color: #0066cc;\">+49 561 804-6259<\/span><\/u><\/a><\/p>\n<h2><span style=\"color: #a3004e;\">Projects<\/span><\/h2>\n<ul>\n<li><a href=\"http:\/\/nameling.net\/\"><u><span style=\"color: #0066cc;\">Nameling<\/span><\/u><\/a> \u2013 An intelligent name browser<\/li>\n<li><a href=\"http:\/\/www.bibsonomy.org\/\"><u><span style=\"color: #0066cc;\">BibSonomy<\/span><\/u><\/a> \u2013 The Blue Social Bookmark and Publication Sharing System<\/li>\n<li><a title=\"Gehe zur Webseite der Discovery Challenge\" href=\"\/ws\/dc13\/\"><u><span style=\"color: #0066cc;\">15th Discovery Challenge<\/span><\/u><\/a> organisiert in Verbindung mit der <a title=\"Gehe zur Konferenzseite\" href=\"http:\/\/www.ecmlpkdd2013.org\/\"><u><span style=\"color: #0066cc;\">ECML PKDD 2013<\/span><\/u><\/a>, 23. \u2013 27. September, 2013, Prag, Tschechien<\/li>\n<\/ul>\n<h2><span style=\"color: #a3004e;\">Publications<\/span><\/h2>\n \n <ul class=\"bibsonomycsl_publications\">\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2014\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2014<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-46f7a831481ba0761e10798be044f691\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-46f7a831481ba0761e10798be044f691\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-46f7a831481ba0761e10798be044f691\"><p>@article{MAHS:14,<br\/>  author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd},<br\/>  journal = {Journal of Social Network Analysis and Mining},<br\/>  keywords = {itegpub},<br\/>  number = 216,<br\/>  title = {{The Social Distributional Hypothesis}},<br\/>  volume = 4,<br\/>  year = 2014<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-46f7a831481ba0761e10798be044f691\"><p>%0 Journal Article<br\/>%1 MAHS:14<br\/>%A Mitzlaff, Folke<br\/>%A Atzmueller, Martin<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%D 2014<br\/>%J Journal of Social Network Analysis and Mining<br\/>%N 216<br\/>%T {The Social Distributional Hypothesis}<br\/>%V 4<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2013\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2013<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/arxiv.org\/abs\/1303.0484\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-0e7c394199e6b0587a880184b206af57\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-0e7c394199e6b0587a880184b206af57\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-0e7c394199e6b0587a880184b206af57\">Onomastics is \"the science or study of the origin and forms of proper names of persons or places.\" [\"Onomastics\". Merriam-Webster.com, 2013. http:\/\/www.merriam-webster.com (11 February 2013)]. Especially personal names play an important role in daily life, as all over the world future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and, in particular, personal taste. With the rise of the Social Web and its applications, users more and more interact digitally and participate in the creation of heterogeneous, distributed, collaborative data collections. These sources of data also reflect current and new naming trends as well as new emerging interrelations among names. The present work shows, how basic approaches from the field of social network analysis and information retrieval can be applied for discovering relations among names, thus extending Onomastics by data mining techniques. The considered approach starts with building co-occurrence graphs relative to data from the Social Web, respectively for given names and city names. As a main result, correlations between semantically grounded similarities among names (e.g., geographical distance for city names) and structural graph based similarities are observed. The discovered relations among given names are the foundation of \"nameling\" [http:\/\/nameling.net], a search engine and academic research platform for given names which attracted more than 30,000 users within four months, underpinningthe relevance of the proposed methodology.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-0e7c394199e6b0587a880184b206af57\"><p>@misc{mitzlaff2013onomastics,<br\/>  abstract = {Onomastics is \"the science or study of the origin and forms of proper names of persons or places.\" [\"Onomastics\". Merriam-Webster.com, 2013. http:\/\/www.merriam-webster.com (11 February 2013)]. Especially personal names play an important role in daily life, as all over the world future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and, in particular, personal taste. With the rise of the Social Web and its applications, users more and more interact digitally and participate in the creation of heterogeneous, distributed, collaborative data collections. These sources of data also reflect current and new naming trends as well as new emerging interrelations among names. The present work shows, how basic approaches from the field of social network analysis and information retrieval can be applied for discovering relations among names, thus extending Onomastics by data mining techniques. The considered approach starts with building co-occurrence graphs relative to data from the Social Web, respectively for given names and city names. As a main result, correlations between semantically grounded similarities among names (e.g., geographical distance for city names) and structural graph based similarities are observed. The discovered relations among given names are the foundation of \"nameling\" [http:\/\/nameling.net], a search engine and academic research platform for given names which attracted more than 30,000 users within four months, underpinningthe relevance of the proposed methodology.},<br\/>  author = {Mitzlaff, Folke and Stumme, Gerd},<br\/>  keywords = {nameling},<br\/>  note = {cite arxiv:1303.0484Comment: Historically, this is the first paper on the analysis of names in the context of the name search engine 'nameling'. arXiv admin note: text overlap with arXiv:1302.4412},<br\/>  title = {Onomastics 2.0 - The Power of Social Co-Occurrences},<br\/>  year = 2013<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-0e7c394199e6b0587a880184b206af57\"><p>%0 Generic<br\/>%1 mitzlaff2013onomastics<br\/>%A Mitzlaff, Folke<br\/>%A Stumme, Gerd<br\/>%D 2013<br\/>%T Onomastics 2.0 - The Power of Social Co-Occurrences<br\/>%U http:\/\/arxiv.org\/abs\/1303.0484<br\/>%X Onomastics is \"the science or study of the origin and forms of proper names of persons or places.\" [\"Onomastics\". Merriam-Webster.com, 2013. http:\/\/www.merriam-webster.com (11 February 2013)]. Especially personal names play an important role in daily life, as all over the world future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and, in particular, personal taste. With the rise of the Social Web and its applications, users more and more interact digitally and participate in the creation of heterogeneous, distributed, collaborative data collections. These sources of data also reflect current and new naming trends as well as new emerging interrelations among names. The present work shows, how basic approaches from the field of social network analysis and information retrieval can be applied for discovering relations among names, thus extending Onomastics by data mining techniques. The considered approach starts with building co-occurrence graphs relative to data from the Social Web, respectively for given names and city names. As a main result, correlations between semantically grounded similarities among names (e.g., geographical distance for city names) and structural graph based similarities are observed. The discovered relations among given names are the foundation of \"nameling\" [http:\/\/nameling.net], a search engine and academic research platform for given names which attracted more than 30,000 users within four months, underpinningthe relevance of the proposed methodology.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-aedf89a1eb405370be2a6d8e0c3be382\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-aedf89a1eb405370be2a6d8e0c3be382\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-aedf89a1eb405370be2a6d8e0c3be382\"><p>@inproceedings{mitzlaff2013leveraging,<br\/>  author = {Mitzlaff, Folke},<br\/>  booktitle = {Proceedings from Sunbelt XXXIII},<br\/>  keywords = {nameling},<br\/>  title = {Name Me If You Can(!) - Leveraging Networks of Given Names},<br\/>  year = 2013<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-aedf89a1eb405370be2a6d8e0c3be382\"><p>%0 Conference Paper<br\/>%1 mitzlaff2013leveraging<br\/>%A Mitzlaff, Folke<br\/>%B Proceedings from Sunbelt XXXIII<br\/>%D 2013<br\/>%T Name Me If You Can(!) - Leveraging Networks of Given Names<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/mitzlaff2013semantics.pdf\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-0a35f1ed66fcd342a6a44d70c63fb735\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-0a35f1ed66fcd342a6a44d70c63fb735\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-0a35f1ed66fcd342a6a44d70c63fb735\">In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by \u201dtweeting\u201d via twitter or implicitly, when the corresponding activities are logged within the application\u2019s internal databases and log files.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-0a35f1ed66fcd342a6a44d70c63fb735\"><p>@incollection{mitzlaff2013semantics,<br\/>  abstract = {In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by \u201dtweeting\u201d via twitter or implicitly, when the corresponding activities are logged within the application\u2019s internal databases and log files.},<br\/>  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},<br\/>  booktitle = {Complex Networks IV},<br\/>  editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert},<br\/>  keywords = {networks},<br\/>  pages = {13-25},<br\/>  publisher = {Springer Berlin Heidelberg},<br\/>  series = {Studies in Computational Intelligence},<br\/>  title = {Semantics of User Interaction in Social Media},<br\/>  volume = 476,<br\/>  year = 2013<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-0a35f1ed66fcd342a6a44d70c63fb735\"><p>%0 Book Section<br\/>%1 mitzlaff2013semantics<br\/>%A Mitzlaff, Folke<br\/>%A Atzmueller, Martin<br\/>%A Stumme, Gerd<br\/>%A Hotho, Andreas<br\/>%B Complex Networks IV<br\/>%D 2013<br\/>%E Ghoshal, Gourab<br\/>%E Poncela-Casasnovas, Julia<br\/>%E Tolksdorf, Robert<br\/>%I Springer Berlin Heidelberg<br\/>%P 13-25<br\/>%R 10.1007\/978-3-642-36844-8_2<br\/>%T Semantics of User Interaction in Social Media<br\/>%U http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/mitzlaff2013semantics.pdf<br\/>%V 476<br\/>%X In ubiquitous and social web applications, there are different user traces, for example, produced explicitly by \u201dtweeting\u201d via twitter or implicitly, when the corresponding activities are logged within the application\u2019s internal databases and log files.<br\/>%@ 978-3-642-36843-1<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/arxiv.org\/abs\/1309.3888\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-cbed5fadde51ddb20c6a470ced93556a\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-cbed5fadde51ddb20c6a470ced93556a\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-cbed5fadde51ddb20c6a470ced93556a\">With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-cbed5fadde51ddb20c6a470ced93556a\"><p>@misc{mitzlaff2013userrelatedness,<br\/>  abstract = {With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.},<br\/>  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},<br\/>  keywords = {networks},<br\/>  note = {cite arxiv:1309.3888},<br\/>  title = {User-Relatedness and Community Structure in Social Interaction Networks},<br\/>  year = 2013<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-cbed5fadde51ddb20c6a470ced93556a\"><p>%0 Generic<br\/>%1 mitzlaff2013userrelatedness<br\/>%A Mitzlaff, Folke<br\/>%A Atzmueller, Martin<br\/>%A Benz, Dominik<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%D 2013<br\/>%T User-Relatedness and Community Structure in Social Interaction Networks<br\/>%U http:\/\/arxiv.org\/abs\/1309.3888<br\/>%X With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/arxiv.org\/abs\/1302.4412\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-41f92650f0f7d78366febc1832cedba9\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-41f92650f0f7d78366febc1832cedba9\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-41f92650f0f7d78366febc1832cedba9\">All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine \"Nameling\" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \\NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-41f92650f0f7d78366febc1832cedba9\"><p>@misc{mitzlaff2013recommending,<br\/>  abstract = {All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine \"Nameling\" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \\NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.},<br\/>  author = {Mitzlaff, Folke and Stumme, Gerd},<br\/>  keywords = {nameling},<br\/>  note = {cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013},<br\/>  title = {Recommending Given Names},<br\/>  year = 2013<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-41f92650f0f7d78366febc1832cedba9\"><p>%0 Generic<br\/>%1 mitzlaff2013recommending<br\/>%A Mitzlaff, Folke<br\/>%A Stumme, Gerd<br\/>%D 2013<br\/>%T Recommending Given Names<br\/>%U http:\/\/arxiv.org\/abs\/1302.4412<br\/>%X All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine \"Nameling\" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \\NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2012\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2012<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/dx.doi.org\/10.1007\/978-3-642-25694-3_3\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-7d41d332cccc3e7ba8e7dadfb7996337\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-7d41d332cccc3e7ba8e7dadfb7996337\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-7d41d332cccc3e7ba8e7dadfb7996337\">Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding\/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-7d41d332cccc3e7ba8e7dadfb7996337\"><p>@incollection{jaeschke2012challenges,<br\/>  abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding\/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.},<br\/>  address = {Berlin\/Heidelberg},<br\/>  author = {J\u00e4schke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},<br\/>  booktitle = {Recommender Systems for the Social Web},<br\/>  editor = {Pazos Arias, Jos\u00e9 J. and Fern\u00e1ndez Vilas, Ana and D\u00edaz Redondo, Rebeca P.},<br\/>  keywords = {recommender},<br\/>  pages = {65--87},<br\/>  publisher = {Springer},<br\/>  series = {Intelligent Systems Reference Library},<br\/>  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},<br\/>  volume = 32,<br\/>  year = 2012<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-7d41d332cccc3e7ba8e7dadfb7996337\"><p>%0 Book Section<br\/>%1 jaeschke2012challenges<br\/>%A J\u00e4schke, Robert<br\/>%A Hotho, Andreas<br\/>%A Mitzlaff, Folke<br\/>%A Stumme, Gerd<br\/>%B Recommender Systems for the Social Web<br\/>%C Berlin\/Heidelberg<br\/>%D 2012<br\/>%E Pazos Arias, Jos\u00e9 J.<br\/>%E Fern\u00e1ndez Vilas, Ana<br\/>%E D\u00edaz Redondo, Rebeca P.<br\/>%I Springer<br\/>%P 65--87<br\/>%R 10.1007\/978-3-642-25694-3_3<br\/>%T Challenges in Tag Recommendations for Collaborative Tagging Systems<br\/>%U http:\/\/dx.doi.org\/10.1007\/978-3-642-25694-3_3<br\/>%V 32<br\/>%X Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding\/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.<br\/>%@ 978-3-642-25694-3<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-c2f599000eaa568ed4d1b0b9d3f6fadd\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-c2f599000eaa568ed4d1b0b9d3f6fadd\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-c2f599000eaa568ed4d1b0b9d3f6fadd\"><p>@inproceedings{mitzlaff2012ranking,<br\/>  author = {Mitzlaff, Folke and Stumme, Gerd},<br\/>  booktitle = {Proceedings of the 1st ASE International Conference on Social Informatics},<br\/>  editor = {Marathe, Madhav and Contractor, Noshir},<br\/>  keywords = {nameling},<br\/>  pages = {185-191},<br\/>  publisher = {IEEE computer society},<br\/>  title = {Ranking Given Names},<br\/>  year = 2012<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-c2f599000eaa568ed4d1b0b9d3f6fadd\"><p>%0 Conference Paper<br\/>%1 mitzlaff2012ranking<br\/>%A Mitzlaff, Folke<br\/>%A Stumme, Gerd<br\/>%B Proceedings of the 1st ASE International Conference on Social Informatics<br\/>%D 2012<br\/>%E Marathe, Madhav<br\/>%E Contractor, Noshir<br\/>%I IEEE computer society<br\/>%P 185-191<br\/>%T Ranking Given Names<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/doi.acm.org\/10.1145\/2365934.2365936\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-200a05b24a08dd33e377838ae5bdcf71\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-200a05b24a08dd33e377838ae5bdcf71\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-200a05b24a08dd33e377838ae5bdcf71\">Real-world tagging datasets have a large proportion of new\/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.<\/p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-200a05b24a08dd33e377838ae5bdcf71\"><p>@inproceedings{landia2012extending,<br\/>  abstract = {Real-world tagging datasets have a large proportion of new\/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.<\/p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.},<br\/>  address = {New York, NY, USA},<br\/>  author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and J\u00e4schke, Robert and Doerfel, Stephan and Mitzlaff, Folke},<br\/>  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},<br\/>  keywords = {content},<br\/>  pages = {1--8},<br\/>  publisher = {ACM},<br\/>  series = {RSWeb '12},<br\/>  title = {Extending FolkRank with content data},<br\/>  year = 2012<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-200a05b24a08dd33e377838ae5bdcf71\"><p>%0 Conference Paper<br\/>%1 landia2012extending<br\/>%A Landia, Nikolas<br\/>%A Anand, Sarabjot Singh<br\/>%A Hotho, Andreas<br\/>%A J\u00e4schke, Robert<br\/>%A Doerfel, Stephan<br\/>%A Mitzlaff, Folke<br\/>%B Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web<br\/>%C New York, NY, USA<br\/>%D 2012<br\/>%I ACM<br\/>%P 1--8<br\/>%R 10.1145\/2365934.2365936<br\/>%T Extending FolkRank with content data<br\/>%U http:\/\/doi.acm.org\/10.1145\/2365934.2365936<br\/>%X Real-world tagging datasets have a large proportion of new\/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.<\/p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.<br\/>%@ 978-1-4503-1638-5<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/mitzlaff2012relatedness.pdf\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-63b13ff16093202c535e5aaac107e567\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-63b13ff16093202c535e5aaac107e567\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-63b13ff16093202c535e5aaac107e567\">As a result of the author's need for help in finding a given namefor the unborn baby, nameling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed nameling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work proposes a new approach for discovering relations among given names, based on co-occurrences within Wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different measures of relatedness among given names are evaluated with respect to nameling's actual usage data. We will show that a modification for the PageRank algorithm overcomes limitations imposed by global network characteristics to preferential PageRank computations. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-63b13ff16093202c535e5aaac107e567\"><p>@article{mitzlaff2012relatedness,<br\/>  abstract = {As a result of the author's need for help in finding a given namefor the unborn baby, nameling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed nameling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work proposes a new approach for discovering relations among given names, based on co-occurrences within Wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different measures of relatedness among given names are evaluated with respect to nameling's actual usage data. We will show that a modification for the PageRank algorithm overcomes limitations imposed by global network characteristics to preferential PageRank computations. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.},<br\/>  author = {Mitzlaff, Folke and Stumme, Gerd},<br\/>  journal = {Human Journal},<br\/>  keywords = {nameling},<br\/>  number = 4,<br\/>  pages = {205-217},<br\/>  publisher = {Academy of Science and Engineering},<br\/>  title = {Relatedness of Given Names},<br\/>  volume = 1,<br\/>  year = 2012<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-63b13ff16093202c535e5aaac107e567\"><p>%0 Journal Article<br\/>%1 mitzlaff2012relatedness<br\/>%A Mitzlaff, Folke<br\/>%A Stumme, Gerd<br\/>%D 2012<br\/>%I Academy of Science and Engineering<br\/>%J Human Journal<br\/>%N 4<br\/>%P 205-217<br\/>%T Relatedness of Given Names<br\/>%U http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/mitzlaff2012relatedness.pdf<br\/>%V 1<br\/>%X As a result of the author's need for help in finding a given namefor the unborn baby, nameling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed nameling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work proposes a new approach for discovering relations among given names, based on co-occurrences within Wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different measures of relatedness among given names are evaluated with respect to nameling's actual usage data. We will show that a modification for the PageRank algorithm overcomes limitations imposed by global network characteristics to preferential PageRank computations. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/mitzlaff2012namelings.pdf\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-e2770a8535fca7cce582148703d8980a\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-e2770a8535fca7cce582148703d8980a\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-e2770a8535fca7cce582148703d8980a\"><p>@inproceedings{mitzlaff2012namelings,<br\/>  author = {Mitzlaff, Folke and Stumme, Gerd},<br\/>  booktitle = {SocInfo},<br\/>  crossref = {conf\/socinfo\/2012},<br\/>  editor = {Aberer, Karl and Flache, Andreas and Jager, Wander and Liu, Ling and Tang, Jie and Gu\u00e9ret, Christophe},<br\/>  keywords = {nameling},<br\/>  pages = {531-534},<br\/>  publisher = {Springer},<br\/>  series = {Lecture Notes in Computer Science},<br\/>  title = {Namelings - Discover Given Name Relatedness Based on Data from the Social Web.},<br\/>  volume = 7710,<br\/>  year = 2012<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-e2770a8535fca7cce582148703d8980a\"><p>%0 Conference Paper<br\/>%1 mitzlaff2012namelings<br\/>%A Mitzlaff, Folke<br\/>%A Stumme, Gerd<br\/>%B SocInfo<br\/>%D 2012<br\/>%E Aberer, Karl<br\/>%E Flache, Andreas<br\/>%E Jager, Wander<br\/>%E Liu, Ling<br\/>%E Tang, Jie<br\/>%E Gu\u00e9ret, Christophe<br\/>%I Springer<br\/>%P 531-534<br\/>%T Namelings - Discover Given Name Relatedness Based on Data from the Social Web.<br\/>%U http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/mitzlaff2012namelings.pdf<br\/>%V 7710<br\/>%@ 978-3-642-35385-7<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2011\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2011<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-be5788e3988f2edeb022f5fbd12097a5\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-be5788e3988f2edeb022f5fbd12097a5\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-be5788e3988f2edeb022f5fbd12097a5\"><p>@inproceedings{atzmueller2011efficient,<br\/>  author = {Atzmueller, Martin and Mitzlaff, Folke},<br\/>  booktitle = {Proc. 24th Intl. FLAIRS Conference},<br\/>  keywords = {itegpub},<br\/>  publisher = {AAAI Press},<br\/>  title = {Efficient Descriptive Community Mining},<br\/>  year = 2011<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-be5788e3988f2edeb022f5fbd12097a5\"><p>%0 Conference Paper<br\/>%1 atzmueller2011efficient<br\/>%A Atzmueller, Martin<br\/>%A Mitzlaff, Folke<br\/>%B Proc. 24th Intl. FLAIRS Conference<br\/>%D 2011<br\/>%I AAAI Press<br\/>%T Efficient Descriptive Community Mining<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/dx.doi.org\/10.1007\/978-3-642-23599-3_5\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-a1c0fd5a9f8c5ddb33b3196927409f36\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-a1c0fd5a9f8c5ddb33b3196927409f36\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-a1c0fd5a9f8c5ddb33b3196927409f36\">Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-a1c0fd5a9f8c5ddb33b3196927409f36\"><p>@incollection{noKey,<br\/>  abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.},<br\/>  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},<br\/>  booktitle = {Analysis of Social Media and Ubiquitous Data},<br\/>  editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin},<br\/>  keywords = {COMMUNE},<br\/>  pages = {79-98},<br\/>  publisher = {Springer Berlin Heidelberg},<br\/>  series = {Lecture Notes in Computer Science},<br\/>  title = {Community Assessment Using Evidence Networks},<br\/>  volume = 6904,<br\/>  year = 2011<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-a1c0fd5a9f8c5ddb33b3196927409f36\"><p>%0 Book Section<br\/>%1 noKey<br\/>%A Mitzlaff, Folke<br\/>%A Atzmueller, Martin<br\/>%A Benz, Dominik<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%B Analysis of Social Media and Ubiquitous Data<br\/>%D 2011<br\/>%E Atzmueller, Martin<br\/>%E Hotho, Andreas<br\/>%E Strohmaier, Markus<br\/>%E Chin, Alvin<br\/>%I Springer Berlin Heidelberg<br\/>%P 79-98<br\/>%R 10.1007\/978-3-642-23599-3_5<br\/>%T Community Assessment Using Evidence Networks<br\/>%U http:\/\/dx.doi.org\/10.1007\/978-3-642-23599-3_5<br\/>%V 6904<br\/>%X Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.<br\/>%@ 978-3-642-23598-6<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-84840e0e94320b5742df381f2ec033b7\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-84840e0e94320b5742df381f2ec033b7\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-84840e0e94320b5742df381f2ec033b7\"><p>@inproceedings{atzmueller2011facetoface,<br\/>  author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},<br\/>  booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation},<br\/>  keywords = {analysis},<br\/>  title = {Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players},<br\/>  year = 2011<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-84840e0e94320b5742df381f2ec033b7\"><p>%0 Conference Paper<br\/>%1 atzmueller2011facetoface<br\/>%A Atzmueller, Martin<br\/>%A Doerfel, Stephan<br\/>%A Hotho, Andreas<br\/>%A Mitzlaff, Folke<br\/>%A Stumme, Gerd<br\/>%B Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation<br\/>%D 2011<br\/>%T Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-0f45e870093c053e6f41f54c14bda46b\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-0f45e870093c053e6f41f54c14bda46b\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-0f45e870093c053e6f41f54c14bda46b\"><p>@inproceedings{mitzlaff2011community,<br\/>  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},<br\/>  booktitle = {Analysis of Social Media and Ubiquitous Data},<br\/>  keywords = {community},<br\/>  series = {LNAI},<br\/>  title = {{Community Assessment using Evidence Networks}},<br\/>  volume = 6904,<br\/>  year = 2011<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-0f45e870093c053e6f41f54c14bda46b\"><p>%0 Conference Paper<br\/>%1 mitzlaff2011community<br\/>%A Mitzlaff, Folke<br\/>%A Atzmueller, Martin<br\/>%A Benz, Dominik<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%B Analysis of Social Media and Ubiquitous Data<br\/>%D 2011<br\/>%T {Community Assessment using Evidence Networks}<br\/>%V 6904<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/dblp.uni-trier.de\/db\/journals\/it\/it53.html#AtzmullerBDHJMMSS11\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-1ceba87cd6bc52faac36247a0c9f52a8\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-1ceba87cd6bc52faac36247a0c9f52a8\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-1ceba87cd6bc52faac36247a0c9f52a8\"><p>@article{atzmueller2011enhancing,<br\/>  author = {Atzm\u00fcller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and J\u00e4schke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd},<br\/>  journal = {it - Information Technology},<br\/>  keywords = {COMMUNE},<br\/>  number = 3,<br\/>  pages = {101-107},<br\/>  title = {Enhancing Social Interactions at Conferences.},<br\/>  volume = 53,<br\/>  year = 2011<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-1ceba87cd6bc52faac36247a0c9f52a8\"><p>%0 Journal Article<br\/>%1 atzmueller2011enhancing<br\/>%A Atzm\u00fcller, Martin<br\/>%A Benz, Dominik<br\/>%A Doerfel, Stephan<br\/>%A Hotho, Andreas<br\/>%A J\u00e4schke, Robert<br\/>%A Macek, Bjoern Elmar<br\/>%A Mitzlaff, Folke<br\/>%A Scholz, Christoph<br\/>%A Stumme, Gerd<br\/>%D 2011<br\/>%J it - Information Technology<br\/>%N 3<br\/>%P 101-107<br\/>%T Enhancing Social Interactions at Conferences.<br\/>%U http:\/\/dblp.uni-trier.de\/db\/journals\/it\/it53.html#AtzmullerBDHJMMSS11<br\/>%V 53<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-dca9a97f90443ccc52e14e40c373bb68\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-dca9a97f90443ccc52e14e40c373bb68\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-dca9a97f90443ccc52e14e40c373bb68\"><p>@inproceedings{mitzlaff2011social,<br\/>  address = {Trade Winds Beach Resort},<br\/>  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},<br\/>  booktitle = {Proceedings from Sunbelt XXXI},<br\/>  keywords = 2011,<br\/>  title = {Structure and Consistency: Assessment of Social Bookmarking Communities},<br\/>  year = 2011<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-dca9a97f90443ccc52e14e40c373bb68\"><p>%0 Conference Paper<br\/>%1 mitzlaff2011social<br\/>%A Mitzlaff, Folke<br\/>%A Atzmueller, Martin<br\/>%A Benz, Dominik<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%B Proceedings from Sunbelt XXXI<br\/>%C Trade Winds Beach Resort<br\/>%D 2011<br\/>%T Structure and Consistency: Assessment of Social Bookmarking Communities<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2010\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2010<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-250d83c41fb10b89c73f54bd7040bd6e\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-250d83c41fb10b89c73f54bd7040bd6e\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-250d83c41fb10b89c73f54bd7040bd6e\">Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{\u00f6}\u00dferer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{\u00e4}rtigkeit, die st{\u00e4}ndige Verf{\u00fc}gbarkeit, aber auch die M{\u00f6}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{\u00fc}nde f{\u00fc}r ihren gegenw{\u00e4}rtigen Erfolg. Der Artikel f{\u00fc}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{\u00e4}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schlie\u00dft mit Querbez{\u00fc}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-250d83c41fb10b89c73f54bd7040bd6e\"><p>@article{HothoBenzEtAl10hmd,<br\/>  abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{\u00f6}\u00dferer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{\u00e4}rtigkeit, die st{\u00e4}ndige Verf{\u00fc}gbarkeit, aber auch die M{\u00f6}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{\u00fc}nde f{\u00fc}r ihren gegenw{\u00e4}rtigen Erfolg. Der Artikel f{\u00fc}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{\u00e4}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schlie\u00dft mit Querbez{\u00fc}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.},<br\/>  author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and J{\u00e4}schke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd},<br\/>  journal = {HMD -- Praxis der Wirtschaftsinformatik},<br\/>  keywords = 2010,<br\/>  month = {02},<br\/>  pages = {47-58},<br\/>  title = {{Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{\u00fc}r Wissenschaftler}},<br\/>  volume = {Heft 271},<br\/>  year = 2010<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-250d83c41fb10b89c73f54bd7040bd6e\"><p>%0 Journal Article<br\/>%1 HothoBenzEtAl10hmd<br\/>%A Hotho, Andreas<br\/>%A Benz, Dominik<br\/>%A Eisterlehner, Folke<br\/>%A J{\u00e4}schke, Robert<br\/>%A Krause, Beate<br\/>%A Schmitz, Christoph<br\/>%A Stumme, Gerd<br\/>%D 2010<br\/>%J HMD -- Praxis der Wirtschaftsinformatik<br\/>%P 47-58<br\/>%T {Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{\u00fc}r Wissenschaftler}<br\/>%V Heft 271<br\/>%X Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{\u00f6}\u00dferer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{\u00e4}rtigkeit, die st{\u00e4}ndige Verf{\u00fc}gbarkeit, aber auch die M{\u00f6}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{\u00fc}nde f{\u00fc}r ihren gegenw{\u00e4}rtigen Erfolg. Der Artikel f{\u00fc}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{\u00e4}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schlie\u00dft mit Querbez{\u00fc}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/dx.doi.org\/10.1007\/s00778-010-0208-4\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-5eb699b2e53803ca9e5fadf22d8b5966\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-5eb699b2e53803ca9e5fadf22d8b5966\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-5eb699b2e53803ca9e5fadf22d8b5966\">Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-5eb699b2e53803ca9e5fadf22d8b5966\"><p>@article{benz2010social,<br\/>  abstract = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.},<br\/>  address = {Berlin \/ Heidelberg},<br\/>  author = {Benz, Dominik and Hotho, Andreas and J\u00e4schke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd},<br\/>  journal = {The VLDB Journal},<br\/>  keywords = {bibsonomy},<br\/>  pages = {849-875},<br\/>  publisher = {Springer},<br\/>  title = {The social bookmark and publication management system bibsonomy},<br\/>  volume = 19,<br\/>  year = 2010<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-5eb699b2e53803ca9e5fadf22d8b5966\"><p>%0 Journal Article<br\/>%1 benz2010social<br\/>%A Benz, Dominik<br\/>%A Hotho, Andreas<br\/>%A J\u00e4schke, Robert<br\/>%A Krause, Beate<br\/>%A Mitzlaff, Folke<br\/>%A Schmitz, Christoph<br\/>%A Stumme, Gerd<br\/>%C Berlin \/ Heidelberg<br\/>%D 2010<br\/>%I Springer<br\/>%J The VLDB Journal<br\/>%P 849-875<br\/>%R 10.1007\/s00778-010-0208-4<br\/>%T The social bookmark and publication management system bibsonomy<br\/>%U http:\/\/dx.doi.org\/10.1007\/s00778-010-0208-4<br\/>%V 19<br\/>%X Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-6596855a55ab198092bc1bc08151632d\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-6596855a55ab198092bc1bc08151632d\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-6596855a55ab198092bc1bc08151632d\"><p>@inproceedings{mitzlaff2010visit,<br\/>  address = {New York, NY, USA},<br\/>  author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas},<br\/>  booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia},<br\/>  keywords = {itegpub},<br\/>  pages = {265--270},<br\/>  publisher = {ACM},<br\/>  series = {HT '10},<br\/>  title = {Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy},<br\/>  year = 2010<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-6596855a55ab198092bc1bc08151632d\"><p>%0 Conference Paper<br\/>%1 mitzlaff2010visit<br\/>%A Mitzlaff, Folke<br\/>%A Benz, Dominik<br\/>%A Stumme, Gerd<br\/>%A Hotho, Andreas<br\/>%B Proceedings of the 21st ACM conference on Hypertext and hypermedia<br\/>%C New York, NY, USA<br\/>%D 2010<br\/>%I ACM<br\/>%P 265--270<br\/>%T Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/www.kde.cs.uni-kassel.de\/ws\/muse2010\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-34d79867b23f41ca2e9f481ee894630f\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-34d79867b23f41ca2e9f481ee894630f\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-34d79867b23f41ca2e9f481ee894630f\">Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-34d79867b23f41ca2e9f481ee894630f\"><p>@inproceedings{mitzlaff2010community,<br\/>  abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.},<br\/>  address = {Barcelona, Spain},<br\/>  author = {Mitzlaff, Folke and Atzm\u00fcller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},<br\/>  booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)},<br\/>  keywords = {COMMUNE},<br\/>  title = {Community Assessment using Evidence Networks},<br\/>  year = 2010<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-34d79867b23f41ca2e9f481ee894630f\"><p>%0 Conference Paper<br\/>%1 mitzlaff2010community<br\/>%A Mitzlaff, Folke<br\/>%A Atzm\u00fcller, Martin<br\/>%A Benz, Dominik<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%B Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)<br\/>%C Barcelona, Spain<br\/>%D 2010<br\/>%T Community Assessment using Evidence Networks<br\/>%U http:\/\/www.kde.cs.uni-kassel.de\/ws\/muse2010<br\/>%X Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-2f61938116e8b239fbff58520ecc284c\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-2f61938116e8b239fbff58520ecc284c\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-2f61938116e8b239fbff58520ecc284c\"><p>@inproceedings{atzmueller2010towards,<br\/>  author = {Atzmueller, Martin and Mitzlaff, Folke},<br\/>  booktitle = {Workshop on Mining Patterns and Subgroups},<br\/>  keywords = {itegpub},<br\/>  publisher = {Lorentz Center, Leiden, The Netherlands. Awarded with the Best Discovery Award},<br\/>  title = {{Towards Mining Descriptive Community Patterns}},<br\/>  year = 2010<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-2f61938116e8b239fbff58520ecc284c\"><p>%0 Conference Paper<br\/>%1 atzmueller2010towards<br\/>%A Atzmueller, Martin<br\/>%A Mitzlaff, Folke<br\/>%B Workshop on Mining Patterns and Subgroups<br\/>%D 2010<br\/>%I Lorentz Center, Leiden, The Netherlands. Awarded with the Best Discovery Award<br\/>%T {Towards Mining Descriptive Community Patterns}<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2009\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2009<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/lwa09.informatik.tu-darmstadt.de\/pub\/KDML\/WebHome\/kdml09_R.Jaeschke_et_al.pdf\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-5e8f40e610e723e966676772aa205f80\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-5e8f40e610e723e966676772aa205f80\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-5e8f40e610e723e966676772aa205f80\">The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-5e8f40e610e723e966676772aa205f80\"><p>@inproceedings{Jaeschke2009,<br\/>  abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.},<br\/>  author = {J\u00e4schke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},<br\/>  booktitle = {Workshop on Knowledge Discovery, Data Mining, and Machine Learning},<br\/>  editor = {Benz, Dominik and Janssen, Frederik},<br\/>  keywords = {bibsonomy},<br\/>  month = {09},<br\/>  pages = {44 --51},<br\/>  title = {Testing and Evaluating Tag Recommenders in a Live System},<br\/>  year = 2009<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-5e8f40e610e723e966676772aa205f80\"><p>%0 Conference Paper<br\/>%1 Jaeschke2009<br\/>%A J\u00e4schke, Robert<br\/>%A Eisterlehner, Folke<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%B Workshop on Knowledge Discovery, Data Mining, and Machine Learning<br\/>%D 2009<br\/>%E Benz, Dominik<br\/>%E Janssen, Frederik<br\/>%P 44 --51<br\/>%T Testing and Evaluating Tag Recommenders in a Live System<br\/>%U http:\/\/lwa09.informatik.tu-darmstadt.de\/pub\/KDML\/WebHome\/kdml09_R.Jaeschke_et_al.pdf<br\/>%X The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation anddevelopment of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance.In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible,open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstratingthe power of the framework.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/ceur-ws.org\/Vol-497\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-c64b98851f270a50717e107d25c9014a\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-c64b98851f270a50717e107d25c9014a\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-c64b98851f270a50717e107d25c9014a\"><p>@proceedings{eisterlehner2009ecmlpkdd,<br\/>  editor = {Eisterlehner, Folke and Hotho, Andreas and J\u00e4schke, Robert},<br\/>  keywords = {dc09},<br\/>  month = {09},<br\/>  series = {CEUR-WS.org},<br\/>  title = {ECML PKDD Discovery Challenge 2009 (DC09)},<br\/>  volume = 497,<br\/>  year = 2009<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-c64b98851f270a50717e107d25c9014a\"><p>%0 Conference Proceedings<br\/>%1 eisterlehner2009ecmlpkdd<br\/>%B CEUR-WS.org<br\/>%D 2009<br\/>%E Eisterlehner, Folke<br\/>%E Hotho, Andreas<br\/>%E J\u00e4schke, Robert<br\/>%T ECML PKDD Discovery Challenge 2009 (DC09)<br\/>%U http:\/\/ceur-ws.org\/Vol-497<br\/>%V 497<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/portal.acm.org\/citation.cfm?doid=1557914.1557969#\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-99cafad8ce2afb5879c6c85c14cc5259\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-99cafad8ce2afb5879c6c85c14cc5259\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-99cafad8ce2afb5879c6c85c14cc5259\">In this demo we present BibSonomy, a social bookmark and publication sharing system.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-99cafad8ce2afb5879c6c85c14cc5259\"><p>@inproceedings{benz2009managing,<br\/>  abstract = {In this demo we present BibSonomy, a social bookmark and publication sharing system.},<br\/>  address = {New York, NY, USA},<br\/>  author = {Benz, Dominik and Eisterlehner, Folke and Hotho, Andreas and J\u00e4schke, Robert and Krause, Beate and Stumme, Gerd},<br\/>  booktitle = {HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia},<br\/>  editor = {Cattuto, Ciro and Ruffo, Giancarlo and Menczer, Filippo},<br\/>  keywords = {bibsonomy},<br\/>  month = {06},<br\/>  pages = {323--324},<br\/>  publisher = {ACM},<br\/>  title = {Managing publications and bookmarks with BibSonomy},<br\/>  year = 2009<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-99cafad8ce2afb5879c6c85c14cc5259\"><p>%0 Conference Paper<br\/>%1 benz2009managing<br\/>%A Benz, Dominik<br\/>%A Eisterlehner, Folke<br\/>%A Hotho, Andreas<br\/>%A J\u00e4schke, Robert<br\/>%A Krause, Beate<br\/>%A Stumme, Gerd<br\/>%B HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia<br\/>%C New York, NY, USA<br\/>%D 2009<br\/>%E Cattuto, Ciro<br\/>%E Ruffo, Giancarlo<br\/>%E Menczer, Filippo<br\/>%I ACM<br\/>%P 323--324<br\/>%R 10.1145\/1557914.1557969<br\/>%T Managing publications and bookmarks with BibSonomy<br\/>%U http:\/\/portal.acm.org\/citation.cfm?doid=1557914.1557969#<br\/>%X In this demo we present BibSonomy, a social bookmark and publication sharing system.<br\/>%@ 978-1-60558-486-7<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_entry\"><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/jaeschke2009testing.pdf\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-21fdf612ba6b356fb1b311fc9369f32d\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-21fdf612ba6b356fb1b311fc9369f32d\" href=\"#\">EndNote<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-21fdf612ba6b356fb1b311fc9369f32d\">The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a \ufffdrst evaluation of two exemplarily deployed recommendation methods.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-21fdf612ba6b356fb1b311fc9369f32d\"><p>@inproceedings{jaeschke2009testing,<br\/>  abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a \ufffdrst evaluation of two exemplarily deployed recommendation methods.},<br\/>  address = {New York, NY, USA},<br\/>  author = {J\u00e4schke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},<br\/>  booktitle = {RecSys '09: Proceedings of the third ACM Conference on Recommender Systems},<br\/>  keywords = {bibsonomy},<br\/>  pages = {369--372},<br\/>  publisher = {ACM},<br\/>  title = {Testing and Evaluating Tag Recommenders in a Live System},<br\/>  year = 2009<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-21fdf612ba6b356fb1b311fc9369f32d\"><p>%0 Conference Paper<br\/>%1 jaeschke2009testing<br\/>%A J\u00e4schke, Robert<br\/>%A Eisterlehner, Folke<br\/>%A Hotho, Andreas<br\/>%A Stumme, Gerd<br\/>%B RecSys '09: Proceedings of the third ACM Conference on Recommender Systems<br\/>%C New York, NY, USA<br\/>%D 2009<br\/>%I ACM<br\/>%P 369--372<br\/>%R 10.1145\/1639714.1639790<br\/>%T Testing and Evaluating Tag Recommenders in a Live System<br\/>%U http:\/\/www.kde.cs.uni-kassel.de\/pub\/pdf\/jaeschke2009testing.pdf<br\/>%X The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a \ufffdrst evaluation of two exemplarily deployed recommendation methods.<br\/>%@ 978-1-60558-435-5<br\/><\/p><\/div><\/div><\/li><\/ul>","protected":false},"excerpt":{"rendered":"<p>University of Kassel FB 16 Electrical Engineering, Computer Science Wilhelmsh\u00f6her Allee 73 34121 Kassel Mail: mitzlaff@cs.uni-kassel.de mitzlaff at cs.uni-kassel.de Tel.: +49 561 804-6250 Fax: +49 561 804-6259 Projects Nameling \u2013 An intelligent name browser BibSonomy<a class=\"moretag\" href=\"https:\/\/www.kde.cs.uni-kassel.de\/en\/mitzlaff\"> Read more&hellip;<\/a><\/p>\n","protected":false},"author":9,"featured_media":0,"parent":0,"menu_order":59,"comment_status":"closed","ping_status":"closed","template":"employee_template.php","meta":{"footnotes":""},"class_list":["post-286","page","type-page","status-publish","hentry"],"translation":{"provider":"WPGlobus","version":"3.0.2","language":"en","enabled_languages":["de","en"],"languages":{"de":{"title":true,"content":true,"excerpt":false},"en":{"title":true,"content":true,"excerpt":false}}},"_links":{"self":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages\/286","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/comments?post=286"}],"version-history":[{"count":12,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages\/286\/revisions"}],"predecessor-version":[{"id":4974,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages\/286\/revisions\/4974"}],"wp:attachment":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/media?parent=286"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}