I am a German researcher that is interested in explainable artificial intelligence. With my research I focus on methods from symbolic methods to generate conceptual views. These are hierarchical structures that encode what abstract concepts are entailed in data and how they depend on each other. The resulting structures are great for structuring data, concept based navigation and rule inference. I contribute to both deeper theoretical insights and applications in the realm of topic models, latent representations of deep learning models, and knowledge representations in general.
In my spare time, I often find myself optimizing my Emacs workflow.
Publications and Preprints
Hirth, J., Horn, V., Stumme, G., Hanika, T.: Automatic Textual Explanations of Concept Lattices In: Ojeda-Aciego, M., Sauerwald, K., and Jäschke, R. (eds.) Graph-Based Representation and Reasoning. pp. 138–152. Springer Nature Switzerland, Cham (2023).
Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.
@inproceedings{10.1007/978-3-031-40960-8_12, abstract = {Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.}, address = {Cham}, author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom}, booktitle = {Graph-Based Representation and Reasoning}, editor = {Ojeda-Aciego, Manuel and Sauerwald, Kai and Jäschke, Robert}, keywords = {sai}, pages = {138--152}, publisher = {Springer Nature Switzerland}, title = {Automatic Textual Explanations of Concept Lattices}, year = 2023 }
%0 Conference Paper %1 10.1007/978-3-031-40960-8_12 %A Hirth, Johannes %A Horn, Viktoria %A Stumme, Gerd %A Hanika, Tom %B Graph-Based Representation and Reasoning %C Cham %D 2023 %E Ojeda-Aciego, Manuel %E Sauerwald, Kai %E Jäschke, Robert %I Springer Nature Switzerland %P 138--152 %T Automatic Textual Explanations of Concept Lattices %X Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales. %@ 978-3-031-40960-8
Ganter, B., Hanika, T., Hirth, J.: Scaling Dimension In: Dürrschnabel, D. and López-Rodríguez, D. (eds.) Formal Concept Analysis - 17th International Conference, {ICFCA} 2023, Kassel, Germany, July 17-21, 2023, Proceedings. pp. 64–77. Springer (2023).
Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.
@misc{hirth2023ordinal, abstract = {Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.}, author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom}, keywords = {sai}, title = {Ordinal Motifs in Lattices}, year = 2023 }
%0 Generic %1 hirth2023ordinal %A Hirth, Johannes %A Horn, Viktoria %A Stumme, Gerd %A Hanika, Tom %D 2023 %T Ordinal Motifs in Lattices %U https://arxiv.org/abs/2304.04827 %X Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.
Hanika, T., Hirth, J.: Conceptual Views on Tree Ensemble Classifiers International Journal of Approximate Reasoning. 108930 (2023).
Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.
@article{https://doi.org/10.48550/arxiv.2302.05270, abstract = {Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.}, author = {Hanika, Tom and Hirth, Johannes}, journal = {International Journal of Approximate Reasoning}, keywords = {sai}, pages = 108930, title = {Conceptual Views on Tree Ensemble Classifiers}, year = 2023 }
%0 Journal Article %1 https://doi.org/10.48550/arxiv.2302.05270 %A Hanika, Tom %A Hirth, Johannes %D 2023 %J International Journal of Approximate Reasoning %P 108930 %R https://doi.org/10.1016/j.ijar.2023.108930 %T Conceptual Views on Tree Ensemble Classifiers %U https://www.sciencedirect.com/science/article/pii/S0888613X23000610 %X Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.
Schäfermeier, B., Hirth, J., Hanika, T.: Research Topic Flows in Co-Authorship Networks Scientometrics. (2022).
In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
@article{schafermeier2022research, abstract = {In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.}, author = {Schäfermeier, Bastian and Hirth, Johannes and Hanika, Tom}, journal = {Scientometrics}, keywords = {topic-models}, month = 10, title = {Research Topic Flows in Co-Authorship Networks}, year = 2022 }
%0 Journal Article %1 schafermeier2022research %A Schäfermeier, Bastian %A Hirth, Johannes %A Hanika, Tom %D 2022 %J Scientometrics %R 10.1007/s11192-022-04529-w %T Research Topic Flows in Co-Authorship Networks %X In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
Hirth, J., Hanika, T.: Formal Conceptual Views in Neural Networks, https://arxiv.org/abs/2209.13517, (2022).
Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.
@article{Hanika2022, abstract = {Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.}, author = {Hanika, Tom and Hirth, Johannes}, journal = {Annals of Mathematics and Artificial Intelligence}, keywords = {sai}, month = {apr}, title = {Knowledge cores in large formal contexts}, year = 2022 }
%0 Journal Article %1 Hanika2022 %A Hanika, Tom %A Hirth, Johannes %D 2022 %J Annals of Mathematics and Artificial Intelligence %R 10.1007/s10472-022-09790-6 %T Knowledge cores in large formal contexts %U https://doi.org/10.1007/s10472-022-09790-6 %X Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.
Hanika, T., Hirth, J.: On the lattice of conceptual measurements Information Sciences. 613, 453–468 (2022).
We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.
@article{hanika2020lattice, abstract = {We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.}, author = {Hanika, Tom and Hirth, Johannes}, journal = {Information Sciences}, keywords = {scale-measure}, pages = {453-468}, title = {On the lattice of conceptual measurements}, volume = 613, year = 2022 }
%0 Journal Article %1 hanika2020lattice %A Hanika, Tom %A Hirth, Johannes %D 2022 %J Information Sciences %P 453-468 %R https://doi.org/10.1016/j.ins.2022.09.005 %T On the lattice of conceptual measurements %U https://www.sciencedirect.com/science/article/pii/S0020025522010489 %V 613 %X We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.
Hanika, T., Hirth, J.: Quantifying the Conceptual Error in Dimensionality Reduction In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings. pp. 105–118. Springer (2021).
@inproceedings{DBLP:conf/iccs/HanikaH21, author = {Hanika, Tom and Hirth, Johannes}, booktitle = {Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings}, editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie}, keywords = {scaling}, pages = {105--118}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Quantifying the Conceptual Error in Dimensionality Reduction}, volume = 12879, year = 2021 }
%0 Conference Paper %1 DBLP:conf/iccs/HanikaH21 %A Hanika, Tom %A Hirth, Johannes %B Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, {ICCS} 2021, Virtual Event, September 20-22, 2021, Proceedings %D 2021 %E Braun, Tanya %E Gehrke, Marcel %E Hanika, Tom %E Hernandez, Nathalie %I Springer %P 105--118 %R 10.1007/978-3-030-86982-3_8 %T Quantifying the Conceptual Error in Dimensionality Reduction %U https://doi.org/10.1007/978-3-030-86982-3_8 %V 12879
Hanika, T., Hirth, J.: Exploring Scale-Measures of Data Sets In: Braud, A., Buzmakov, A., Hanika, T., and Ber, F.L. (eds.) Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings. pp. 261–269. Springer (2021).
@inproceedings{DBLP:conf/icfca/HanikaH21, author = {Hanika, Tom and Hirth, Johannes}, booktitle = {Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings}, editor = {Braud, Agn{{è}}s and Buzmakov, Aleksey and Hanika, Tom and Ber, Florence Le}, keywords = {scaling}, pages = {261--269}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Exploring Scale-Measures of Data Sets}, volume = 12733, year = 2021 }
%0 Conference Paper %1 DBLP:conf/icfca/HanikaH21 %A Hanika, Tom %A Hirth, Johannes %B Formal Concept Analysis - 16th International Conference, {ICFCA} 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings %D 2021 %E Braud, Agn{{è}}s %E Buzmakov, Aleksey %E Hanika, Tom %E Ber, Florence Le %I Springer %P 261--269 %R 10.1007/978-3-030-77867-5_17 %T Exploring Scale-Measures of Data Sets %U https://doi.org/10.1007/978-3-030-77867-5_17 %V 12733
Hanika, T., Hirth, J.: Conexp-Clj - A Research Tool for FCA. In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 70–75. CEUR-WS.org (2019).
@inproceedings{conf/icfca/HanikaH19, author = {Hanika, Tom and Hirth, Johannes}, booktitle = {ICFCA (Supplements)}, crossref = {conf/icfca/2019suppl}, editor = {Cristea, Diana and Ber, Florence Le and Missaoui, Rokia and Kwuida, Léonard and Sertkaya, Baris}, keywords = {sai}, pages = {70-75}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Conexp-Clj - A Research Tool for FCA.}, volume = 2378, year = 2019 }
%0 Conference Paper %1 conf/icfca/HanikaH19 %A Hanika, Tom %A Hirth, Johannes %B ICFCA (Supplements) %D 2019 %E Cristea, Diana %E Ber, Florence Le %E Missaoui, Rokia %E Kwuida, Léonard %E Sertkaya, Baris %I CEUR-WS.org %P 70-75 %T Conexp-Clj - A Research Tool for FCA. %U http://dblp.uni-trier.de/db/conf/icfca/icfca2019suppl.html#HanikaH19 %V 2378
Talks
July 2023: ‚Scaling Dimension‘, ICFCA 2023, University of Kassel, Kassel, Germany
July 2021: ‚Exploring Scale-Measures of Data Sets‘, ICFCA (2021), Université de Strasbourg, Strasbourg, France
March 2021: ‚Discovery of Conceptual Measurements/Entdecken Begrifflicher Messungen‘, Explainable Artificial Intelligence, Schloss Dagstuhl Computer Science Center, Wadern, Germany
Subreviewer: 11th ACM conference on Web Science, June 30–July 3, 2019, Boston, MA, USA
Teaching Assistence
Functional Programming in Clojure: 2019 — ongoing
Conceptual Data Analysis (Labor): 2019 — 2023
Conceptual Data Analysis: 2022
Artificial Intelligence: 2022
Knowledge Discovery: 2021
Social Network Analysis (Labor): 2020
Databases: 2020
Projects
Accompanying my theoretical research, there are two projects I mainly work for:
Conexp-Clj — A Research Tool for FCA
The research unit Knowledge & Data Engineering continues the development of the research tool conexp-clj, originally created by Dr. Daniel Borchmann. The continuous enhancement of the software package is supervised by Dr. Tom Hanika. Having such a tool at hand, the research group is able to test and analyze the theoretical research efforts in the realm of formal concept analysis and related fields. The most recent, pre-compiled, release candidate can be downloaded here. A presentation of the tool can be found in Conexp-Clj – A Research Tool for FCA
BibSonomy is a scholarly social bookmarking system where researchers manage their collections of publications and web pages. BibSonomy is an open source project, continously developed by researchers in Kassel, Würzburg, and Hanover. Functioning as a test bed for recommendation and ranking algorithms, as well as through the publicly available datasets, containing traces of user behavior on the Web, BibSonomy has been the subject of various scientific studies.
Im Projekt faire digitale Dienste: „Ko-Valuation in der Gestaltung datenökonomischer Geschäftsmodelle (FAIRDIENSTE)“ wird ein interdisziplinärer Ansatz verfolgt, der sowohl soziologische als auch (wirtschafts-)informatische Aspekte beinhaltet. Es werden faire Geschäftsmodelle untersucht, die auf Kooperation und Wertevermittlung zielen.
Ein Ziel der Arbeit ist die Weiterentwicklung informatischer Methoden zur qualitativen Datenanalyse, welche die an den Kundenschnittstellen digitaler Dienste auftretende Konfliktlandschaft transparent machen und die für Verbraucher*innen eine kritische Beurteilung verschiedener Wertgesichtspunkte ermöglichen soll.
We provide an interactive WebApp (Link) accompanying our work on the Topic Flow Network, which enables us to study topic specific flow of expertise between scientific authors.
Associated Papers:
Schäfermeier, B., Hirth, J., & Hanika, T. (2022). Research topic flows in co-authorship networks. Scientometrics, 1-28.
During the documenta 2022, there is a 100 days of STEM event hosted by the Schülerforschungszentrum Nordhessen, a research centre for students. We participated with a workshop on Which is better? Scale, organise and understand data properly.
Supervision of Student Internships
I supervise an annual two-week student internship at the University of Kassel on simulating traffic using cellular automata (2016) and analyzing the twitter social network (2018 — today).
Teaching Programming in Clojure
Teaching an annual Clojure Programming Course (2019 — today).
