Fachgebiet Wissensverarbeitung (KDE), EECS, Universität Kassel
Das Fachgebiet Wissensverarbeitung des Fachbereichs Elektrotechnik/Informatik forscht an der Entwicklung von Methoden zur Wissensentdeckung und Wissensrepräsentation (Approximation und Exploration von Wissen, Ordnungsstrukturen in Wissen, Ontologieentwicklung) in Daten als auch in der Analyse von (sozialen) Netzwerkdaten und damit verbundenen Wissensprozessen (Metriken in Netzwerken, Anomalieerkennung, Charakterisierung von sozialen Netzwerken). Dabei liegt ein Schwerpunkt auf der exakten algebraischen Modellierung der verwendeten Strukturen und auf der Evaluierung und Neuentwicklung von Netzwerkmaßen. Neben der Erforschung von Grundlagen in den Gebieten Ordnungs- und Verbandstheorie, Beschreibungslogiken, Graphentheorie und Ontologie werden auch Anwendungen – bspw. in sozialen Medien sowie in der Szientometrie – erforscht.
Das Fachgebiet Wissensverarbeitung ist Mitglied im Wissenschaftlichen Zentrum für Informationstechnik-Gestaltung (ITeG) der Universität Kassel, im Wissenschaftlichen Zentrum INCHER der Universität Kassel und im Hessischen KI-Zentrum (hessian.AI).
Unsere neusten Publikationen
- 1.Draude, C., Engert, S., Hess, T., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M., Zwingmann, N.: Verrechnung – Design – Kultivierung: Instrumentenkasten für die Gestaltung fairer Geschäftsmodelle durch Ko-Valuation, https://plattform-privatheit.de/p-prv-wAssets/Assets/Veroeffentlichungen_WhitePaper_PolicyPaper/whitepaper/WP_2024_FAIRDIENSTE_1.0.pdf, (2024). https://doi.org/10.24406/publica-2497.
@misc{claude2024verrechnung,
address = {Karlsruhe},
author = {Draude, Claude and Engert, Simon and Hess, Thomas and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, Jörn and Stumme, Gerd and Uhlmann, Markus and Zwingmann, Nina},
edition = 1,
editor = {Friedewald, Michael and Roßnagel, Alexander and Geminn, Christian and Karaboga, Murat},
howpublished = {White Paper},
keywords = {itegpub},
month = {03},
publisher = {Fraunhofer-Institut für System- und Innovationsforschung ISI},
series = {Plattform Privatheit},
title = {Verrechnung – Design – Kultivierung: Instrumentenkasten für die Gestaltung fairer Geschäftsmodelle durch Ko-Valuation},
year = 2024
}%0 Generic
%1 claude2024verrechnung
%A Draude, Claude
%A Engert, Simon
%A Hess, Thomas
%A Hirth, Johannes
%A Horn, Viktoria
%A Kropf, Jonathan
%A Lamla, Jörn
%A Stumme, Gerd
%A Uhlmann, Markus
%A Zwingmann, Nina
%B Plattform Privatheit
%C Karlsruhe
%D 2024
%E Friedewald, Michael
%E Roßnagel, Alexander
%E Geminn, Christian
%E Karaboga, Murat
%I Fraunhofer-Institut für System- und Innovationsforschung ISI
%R 10.24406/publica-2497
%T Verrechnung – Design – Kultivierung: Instrumentenkasten für die Gestaltung fairer Geschäftsmodelle durch Ko-Valuation
%U https://plattform-privatheit.de/p-prv-wAssets/Assets/Veroeffentlichungen_WhitePaper_PolicyPaper/whitepaper/WP_2024_FAIRDIENSTE_1.0.pdf
%7 1 - 1.Hanika, T., Hille, T.: What is the intrinsic dimension of your binary data? -- and how to compute it quickly, (2024).
@misc{hanika2024textitintrinsic,
author = {Hanika, Tom and Hille, Tobias},
keywords = {itegpub},
title = {What is the intrinsic dimension of your binary data? -- and how to compute it quickly},
year = 2024
}%0 Generic
%1 hanika2024textitintrinsic
%A Hanika, Tom
%A Hille, Tobias
%D 2024
%T What is the intrinsic dimension of your binary data? -- and how to compute it quickly - 1.Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal motifs in lattices. Information Sciences. 659, 120009 (2024). https://doi.org/https://doi.org/10.1016/j.ins.2023.120009.
@article{HIRTH2024120009,
author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},
journal = {Information Sciences},
keywords = {itegpub},
pages = 120009,
title = {Ordinal motifs in lattices},
volume = 659,
year = 2024
}%0 Journal Article
%1 HIRTH2024120009
%A Hirth, Johannes
%A Horn, Viktoria
%A Stumme, Gerd
%A Hanika, Tom
%D 2024
%J Information Sciences
%P 120009
%R https://doi.org/10.1016/j.ins.2023.120009
%T Ordinal motifs in lattices
%U https://www.sciencedirect.com/science/article/pii/S0020025523015943
%V 659 - 1.Abdulla, M., Hirth, J., Stumme, G.: The Birkhoff completion of finite lattices, (2024).
@misc{abdulla2024birkhoff,
author = {Abdulla, Mohammad and Hirth, Johannes and Stumme, Gerd},
keywords = {fca},
title = {The Birkhoff completion of finite lattices},
year = 2024
}%0 Generic
%1 abdulla2024birkhoff
%A Abdulla, Mohammad
%A Hirth, Johannes
%A Stumme, Gerd
%D 2024
%T The Birkhoff completion of finite lattices - 1.Hille, T., Stubbemann, M., Hanika, T.: Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research, (2024).
@preprint{hille2024reproducibility,
author = {Hille, Tobias and Stubbemann, Maximilian and Hanika, Tom},
keywords = {kde},
title = {Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research},
year = 2024
}%0 Generic
%1 hille2024reproducibility
%A Hille, Tobias
%A Stubbemann, Maximilian
%A Hanika, Tom
%D 2024
%T Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research - 1.Hirth, J., Hanika, T.: The Geometric Structure of Topic Models, (2024). https://doi.org/10.48550/arxiv.2403.03607.
@misc{hirth2024geometric,
author = {Hirth, Johannes and Hanika, Tom},
keywords = {selected},
publisher = {arXiv},
title = {The Geometric Structure of Topic Models},
year = 2024
}%0 Generic
%1 hirth2024geometric
%A Hirth, Johannes
%A Hanika, Tom
%D 2024
%I arXiv
%R 10.48550/arxiv.2403.03607
%T The Geometric Structure of Topic Models - 1.Dürrschnabel, D., Priss, U.: Realizability of Rectangular Euler Diagrams, (2024).
@misc{dürrschnabel2024realizability,
author = {Dürrschnabel, Dominik and Priss, Uta},
keywords = {itegpub},
title = {Realizability of Rectangular Euler Diagrams},
year = 2024
}%0 Generic
%1 dürrschnabel2024realizability
%A Dürrschnabel, Dominik
%A Priss, Uta
%D 2024
%T Realizability of Rectangular Euler Diagrams - 1.Draude, C., Dürrschnabel, D., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M.: Conceptual Mapping of Controversies, (2024).With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles
@misc{draude2024conceptual,
abstract = {With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles},
author = {Draude, Claude and Dürrschnabel, Dominik and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, Jörn and Stumme, Gerd and Uhlmann, Markus},
keywords = {itegpub},
title = {Conceptual Mapping of Controversies},
year = 2024
}%0 Generic
%1 draude2024conceptual
%A Draude, Claude
%A Dürrschnabel, Dominik
%A Hirth, Johannes
%A Horn, Viktoria
%A Kropf, Jonathan
%A Lamla, Jörn
%A Stumme, Gerd
%A Uhlmann, Markus
%D 2024
%T Conceptual Mapping of Controversies
%X With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles - 1.Dürrschnabel, D., Hanika, T., Stumme, G.: Drawing Order Diagrams Through Two-Dimension Extension. Journal of Graph Algorithms and Applications. 27, 783–802 (2023). https://doi.org/10.7155/jgaa.00645.
@article{drrschnabel2023drawing,
author = {Dürrschnabel, Dominik and Hanika, Tom and Stumme, Gerd},
journal = {Journal of Graph Algorithms and Applications},
keywords = {linear_extension},
number = 9,
pages = {783–802},
publisher = {Journal of Graph Algorithms and Applications},
title = {Drawing Order Diagrams Through Two-Dimension Extension},
volume = 27,
year = 2023
}%0 Journal Article
%1 drrschnabel2023drawing
%A Dürrschnabel, Dominik
%A Hanika, Tom
%A Stumme, Gerd
%D 2023
%I Journal of Graph Algorithms and Applications
%J Journal of Graph Algorithms and Applications
%N 9
%P 783–802
%R 10.7155/jgaa.00645
%T Drawing Order Diagrams Through Two-Dimension Extension
%U http://dx.doi.org/10.7155/jgaa.00645
%V 27 - 1.Stumme, G., Dürrschnabel, D., Hanika, T.: Towards Ordinal Data Science. Transactions on Graph Data and Knowledge. 1, 6:1–6:39 (2023). https://doi.org/10.4230/TGDK.1.1.6.
@article{DBLP:journals/tgdk/StummeDH23,
author = {Stumme, Gerd and Dürrschnabel, Dominik and Hanika, Tom},
journal = {Transactions on Graph Data and Knowledge},
keywords = {itegpub},
number = 1,
pages = {6:1--6:39},
title = {Towards Ordinal Data Science},
volume = 1,
year = 2023
}%0 Journal Article
%1 DBLP:journals/tgdk/StummeDH23
%A Stumme, Gerd
%A Dürrschnabel, Dominik
%A Hanika, Tom
%D 2023
%J Transactions on Graph Data and Knowledge
%N 1
%P 6:1--6:39
%R 10.4230/TGDK.1.1.6
%T Towards Ordinal Data Science
%U https://doi.org/10.4230/TGDK.1.1.6
%V 1 - 1.Stubbemann, M., Hille, T., Hanika, T.: Selecting Features by their Resilience to the Curse of Dimensionality. (2023).
@article{stubbemann2023selecting,
author = {Stubbemann, Maximilian and Hille, Tobias and Hanika, Tom},
keywords = {selecting},
title = {Selecting Features by their Resilience to the Curse of Dimensionality},
year = 2023
}%0 Journal Article
%1 stubbemann2023selecting
%A Stubbemann, Maximilian
%A Hille, Tobias
%A Hanika, Tom
%D 2023
%T Selecting Features by their Resilience to the Curse of Dimensionality - 1.Koyda, M., Stumme, G.: Factorizing Lattices by Interval Relations. Int. J. Approx. Reason. 157, 70–87 (2023).
@article{koyda2023factorizing,
author = {Koyda, Maren and Stumme, Gerd},
journal = {Int. J. Approx. Reason.},
keywords = 2023,
pages = {70-87},
title = {Factorizing Lattices by Interval Relations},
volume = 157,
year = 2023
}%0 Journal Article
%1 koyda2023factorizing
%A Koyda, Maren
%A Stumme, Gerd
%D 2023
%J Int. J. Approx. Reason.
%P 70-87
%T Factorizing Lattices by Interval Relations
%U http://dblp.uni-trier.de/db/journals/ijar/ijar157.html#KoydaS23
%V 157 - 1.Felde, M., Stumme, G.: Interactive collaborative exploration using incomplete contexts. Data & Knowledge Engineering. 143, 102104 (2023). https://doi.org/10.1016/j.datak.2022.102104.
@article{Felde_2023,
author = {Felde, Maximilian and Stumme, Gerd},
journal = {Data & Knowledge Engineering},
keywords = {itegpub},
month = {01},
pages = 102104,
publisher = {Elsevier {BV}},
title = {Interactive collaborative exploration using incomplete contexts},
volume = 143,
year = 2023
}%0 Journal Article
%1 Felde_2023
%A Felde, Maximilian
%A Stumme, Gerd
%D 2023
%I Elsevier {BV}
%J Data & Knowledge Engineering
%P 102104
%R 10.1016/j.datak.2022.102104
%T Interactive collaborative exploration using incomplete contexts
%U https://doi.org/10.1016%2Fj.datak.2022.102104
%V 143 - 1.Stubbemann, M., Hanika, T., Schneider, F.M.: Intrinsic Dimension for Large-Scale Geometric Learning. Transactions on Machine Learning Research. (2023).
@article{stubbemann2022intrinsic,
author = {Stubbemann, Maximilian and Hanika, Tom and Schneider, Friedrich Martin},
journal = {Transactions on Machine Learning Research},
keywords = {itegpub},
title = {Intrinsic Dimension for Large-Scale Geometric Learning},
year = 2023
}%0 Journal Article
%1 stubbemann2022intrinsic
%A Stubbemann, Maximilian
%A Hanika, Tom
%A Schneider, Friedrich Martin
%D 2023
%J Transactions on Machine Learning Research
%T Intrinsic Dimension for Large-Scale Geometric Learning
%U https://openreview.net/forum?id=85BfDdYMBY - 1.Felde, M., Koyda, M.: Interval-dismantling for lattices. International Journal of Approximate Reasoning. 159, 108931 (2023). https://doi.org/10.1016/j.ijar.2023.108931.Dismantling allows for the removal of elements from a poset, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique core with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.
@article{FELDE2023108931,
abstract = {Dismantling allows for the removal of elements from a poset, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique core with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals.},
author = {Felde, Maximilian and Koyda, Maren},
journal = {International Journal of Approximate Reasoning},
keywords = {Concept_lattice},
pages = 108931,
title = {Interval-dismantling for lattices},
volume = 159,
year = 2023
}%0 Journal Article
%1 FELDE2023108931
%A Felde, Maximilian
%A Koyda, Maren
%D 2023
%J International Journal of Approximate Reasoning
%P 108931
%R 10.1016/j.ijar.2023.108931
%T Interval-dismantling for lattices
%U https://www.sciencedirect.com/science/article/pii/S0888613X23000622
%V 159
%X Dismantling allows for the removal of elements from a poset, or in our case lattice, without disturbing the remaining structure. In this paper we have extended the notion of dismantling by single elements to the dismantling by intervals in a lattice. We utilize theory from Formal Concept Analysis (FCA) to show that lattices dismantled by intervals correspond to closed subrelations in the respective formal context, and that there exists a unique core with respect to dismantling by intervals. Furthermore, we show that dismantling intervals can be identified directly in the formal context utilizing a characterization via arrow relations and provide an algorithm to compute all dismantling intervals. - 1.Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal Motifs in Lattices, http://arxiv.org/abs/2304.04827, (2023). https://doi.org/10.48550/arXiv.2304.04827.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 = {ordinal},
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
%R 10.48550/arXiv.2304.04827
%T Ordinal Motifs in Lattices
%U http://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. - 1.Stubbemann, M., Stumme, G.: The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. In: Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, {ECML} {PKDD} 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part {III}. pp. 177–192. Springer (2023). https://doi.org/10.1007/978-3-031-43418-1\_11.
@inproceedings{DBLP:conf/pkdd/StubbemannS23,
author = {Stubbemann, Maximilian and Stumme, Gerd},
booktitle = {Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, {ECML} {PKDD} 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part {III}},
keywords = {itegpub},
pages = {177--192},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks},
volume = 14171,
year = 2023
}%0 Conference Paper
%1 DBLP:conf/pkdd/StubbemannS23
%A Stubbemann, Maximilian
%A Stumme, Gerd
%B Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, {ECML} {PKDD} 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part {III}
%D 2023
%I Springer
%P 177--192
%R 10.1007/978-3-031-43418-1\_11
%T The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks
%U https://doi.org/10.1007/978-3-031-43418-1_11
%V 14171 - 1.Schäfermeier, B., Hirth, J., Hanika, T.: Research Topic Flows in Co-Authorship Networks. Scientometrics. 128, 5051–5078 (2023). https://doi.org/10.1007/s11192-022-04529-w.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 = {co-authorships},
month = {09},
number = 9,
pages = {5051--5078},
title = {Research Topic Flows in Co-Authorship Networks},
volume = 128,
year = 2023
}%0 Journal Article
%1 schafermeier2022research
%A Schäfermeier, Bastian
%A Hirth, Johannes
%A Hanika, Tom
%D 2023
%J Scientometrics
%N 9
%P 5051--5078
%R 10.1007/s11192-022-04529-w
%T Research Topic Flows in Co-Authorship Networks
%V 128
%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. - 1.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 - 28th International Conference on Conceptual Structures, {ICCS} 2023, Berlin, Germany, September 11-13, 2023, Proceedings. pp. 138–152 (2023). https://doi.org/doi.org/10.1007/978-3-031-40960-8_12.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{hirth2023automatic,
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.},
author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},
booktitle = {Graph-Based Representation and Reasoning - 28th International Conference on Conceptual Structures, {ICCS} 2023, Berlin, Germany, September 11-13, 2023, Proceedings},
editor = {Ojeda{-}Aciego, Manuel and Sauerwald, Kai and Jäschke, Robert},
keywords = {itegpub},
pages = {138--152},
title = {Automatic Textual Explanations of Concept Lattices},
volume = 14133,
year = 2023
}%0 Conference Paper
%1 hirth2023automatic
%A Hirth, Johannes
%A Horn, Viktoria
%A Stumme, Gerd
%A Hanika, Tom
%B Graph-Based Representation and Reasoning - 28th International Conference on Conceptual Structures, {ICCS} 2023, Berlin, Germany, September 11-13, 2023, Proceedings
%D 2023
%E Ojeda{-}Aciego, Manuel
%E Sauerwald, Kai
%E Jäschke, Robert
%P 138--152
%R doi.org/10.1007/978-3-031-40960-8_12
%T Automatic Textual Explanations of Concept Lattices
%U http://arxiv.org/abs/2304.08093
%V 14133
%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.