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) und im Wissenschaftlichen Zentrum INCHER der Universität Kassel, sowie im Hessischen KI-Zentrum (hessian.AI).

Unsere neusten Publikationen

• 1.

  Tolzin, A., Hille, T., Knoth, N., Janson, A.: Mining Hidden Prompt Engineering Patterns with Formal Concept Analysis and Association Rules. In: Bui, T.X. (ed.) 59th Hawaii International Conference on System Sciences, {HICSS} 2026. ScholarSpace (2026).

  URLBibTeXEndNote

  @inproceedings{Tolzin2026Jan,
  author = {Tolzin, Antonia and Hille, Tobias and Knoth, Nils and Janson, Andreas},
  booktitle = {59th Hawaii International Conference on System Sciences, {HICSS} 2026},
  editor = {Bui, Tung X.},
  keywords = {itegpub},
  month = {01},
  publisher = {ScholarSpace},
  title = {Mining Hidden Prompt Engineering Patterns with Formal Concept Analysis and Association Rules},
  year = 2026
  }
  

  %0 Conference Paper
  %1 Tolzin2026Jan
  %A Tolzin, Antonia
  %A Hille, Tobias
  %A Knoth, Nils
  %A Janson, Andreas
  %B 59th Hawaii International Conference on System Sciences, {HICSS} 2026
  %D 2026
  %E Bui, Tung X.
  %I ScholarSpace
  %T Mining Hidden Prompt Engineering Patterns with Formal Concept Analysis and Association Rules
  %U https://hdl.handle.net/10125/111529
  %@ 978-0-9981331-9-5
  

• 1.

  Abdulla, M., Hille, T., Dürrschnabel, D., Stumme, G.: Rises for Measuring Local Distributivity in Lattices. In: Cellier, P., Ganter, B., and Missaoui, R. (eds.) SpringerLink. pp. 392–407. Springer, Cham, Switzerland (2025). https://doi.org/10.1007/978-3-032-03364-2_25.

  URLBibTeXEndNoteDOI

  @incollection{Abdulla2025Sep,
  address = {Cham, Switzerland},
  author = {Abdulla, Mohammad and Hille, Tobias and Dürrschnabel, Dominik and Stumme, Gerd},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cellier, Peggy and Ganter, Bernhard and Missaoui, Rokia},
  journal = {SpringerLink},
  keywords = {itegpub},
  month = {09},
  pages = {392--407},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Rises for Measuring Local Distributivity in Lattices},
  type = {Publication},
  volume = 15941,
  year = 2025
  }
  

  %0 Book Section
  %1 Abdulla2025Sep
  %A Abdulla, Mohammad
  %A Hille, Tobias
  %A Dürrschnabel, Dominik
  %A Stumme, Gerd
  %B Conceptual Knowledge Structures
  %C Cham, Switzerland
  %D 2025
  %E Cellier, Peggy
  %E Ganter, Bernhard
  %E Missaoui, Rokia
  %I Springer
  %J SpringerLink
  %P 392--407
  %R 10.1007/978-3-032-03364-2_25
  %T Rises for Measuring Local Distributivity in Lattices
  %U https://dblp.uni-trier.de/rec/conf/concepts/AbdullaHDS25.html
  %V 15941
  %@ 978-3-032-03364-2
  

• 1.

  Hirth, J., Hanika, T.: The Geometric Structure of Topic Models. In: Cellier, P., Ganter, B., and Missaoui, R. (eds.) CONCEPTS. pp. 19–34. Springer (2025).

  URLBibTeXEndNote

  @inproceedings{conf/concepts/HirthH25,
  author = {Hirth, Johannes and Hanika, Tom},
  booktitle = {CONCEPTS},
  crossref = {conf/concepts/2025},
  editor = {Cellier, Peggy and Ganter, Bernhard and Missaoui, Rokia},
  keywords = {itegpub},
  pages = {19-34},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {The Geometric Structure of Topic Models.},
  volume = 15941,
  year = 2025
  }
  

  %0 Conference Paper
  %1 conf/concepts/HirthH25
  %A Hirth, Johannes
  %A Hanika, Tom
  %B CONCEPTS
  %D 2025
  %E Cellier, Peggy
  %E Ganter, Bernhard
  %E Missaoui, Rokia
  %I Springer
  %P 19-34
  %T The Geometric Structure of Topic Models.
  %U http://dblp.uni-trier.de/db/conf/concepts/concepts2025.html#HirthH25
  %V 15941
  %@ 978-3-032-03364-2
  

• 1.

  Abdulla, M., Hille, T., D{ü}rrschnabel, D., Stumme, G.: Rises for Measuring Local Distributivity in Lattices. In: Cellier, P., Ganter, B., and Missaoui, R. (eds.) Conceptual Knowledge Structures. pp. 392–407. Springer Nature Switzerland, Cham (2025).

  BibTeXEndNote

  Distributivity is a well-established and extensively studied notion in lattice theory. In the context of data analysis, particularly within Formal Concept Analysis (FCA), lattices are often observed to exhibit a high degree of distributivity. However, no standardized measure exists to quantify this property. In this paper, we introduce the notion of rises in (concept) lattices as a means to assess distributivity. Rises capture how the number of attributes or objects in covering concepts change within the concept lattice. We show that a lattice is distributive if and only if no non-unit rises occur. Furthermore, we relate rises to the classical notion of meet- and join distributivity. We observe that concept lattices from real-world data are to a high degree join-distributive, but much less meet-distributive. We additionally study how join-distributivity manifests on the level of ordered sets.

  @inproceedings{10.1007/978-3-032-03364-2_25,
  abstract = {Distributivity is a well-established and extensively studied notion in lattice theory. In the context of data analysis, particularly within Formal Concept Analysis (FCA), lattices are often observed to exhibit a high degree of distributivity. However, no standardized measure exists to quantify this property. In this paper, we introduce the notion of rises in (concept) lattices as a means to assess distributivity. Rises capture how the number of attributes or objects in covering concepts change within the concept lattice. We show that a lattice is distributive if and only if no non-unit rises occur. Furthermore, we relate rises to the classical notion of meet- and join distributivity. We observe that concept lattices from real-world data are to a high degree join-distributive, but much less meet-distributive. We additionally study how join-distributivity manifests on the level of ordered sets.},
  address = {Cham},
  author = {Abdulla, Mohammad and Hille, Tobias and D{ü}rrschnabel, Dominik and Stumme, Gerd},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cellier, Peggy and Ganter, Bernhard and Missaoui, Rokia},
  keywords = {itegpub},
  pages = {392--407},
  publisher = {Springer Nature Switzerland},
  title = {Rises for Measuring Local Distributivity in Lattices},
  year = 2025
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-032-03364-2_25
  %A Abdulla, Mohammad
  %A Hille, Tobias
  %A D{ü}rrschnabel, Dominik
  %A Stumme, Gerd
  %B Conceptual Knowledge Structures
  %C Cham
  %D 2025
  %E Cellier, Peggy
  %E Ganter, Bernhard
  %E Missaoui, Rokia
  %I Springer Nature Switzerland
  %P 392--407
  %T Rises for Measuring Local Distributivity in Lattices
  %X Distributivity is a well-established and extensively studied notion in lattice theory. In the context of data analysis, particularly within Formal Concept Analysis (FCA), lattices are often observed to exhibit a high degree of distributivity. However, no standardized measure exists to quantify this property. In this paper, we introduce the notion of rises in (concept) lattices as a means to assess distributivity. Rises capture how the number of attributes or objects in covering concepts change within the concept lattice. We show that a lattice is distributive if and only if no non-unit rises occur. Furthermore, we relate rises to the classical notion of meet- and join distributivity. We observe that concept lattices from real-world data are to a high degree join-distributive, but much less meet-distributive. We additionally study how join-distributivity manifests on the level of ordered sets.
  %@ 978-3-032-03364-2
  

• 1.

  Gutekunst, K.M., D{ü}rrschnabel, D., Hirth, J., Stumme, G.: Conceptual Topic Aggregation. In: Cellier, P., Ganter, B., and Missaoui, R. (eds.) Conceptual Knowledge Structures. pp. 3–18. Springer Nature Switzerland, Cham (2025).

  BibTeXEndNote

  The vast growth of data has rendered traditional manual inspection infeasible, necessitating the adoption of computational methods for efficient data exploration. Topic modeling has emerged as a powerful tool for analyzing large-scale textual datasets, enabling the extraction of latent semantic structures. However, existing methods for topic modeling often struggle to provide interpretable representations that facilitate deeper insights into data structure and content.

  @inproceedings{10.1007/978-3-032-03364-2_1,
  abstract = {The vast growth of data has rendered traditional manual inspection infeasible, necessitating the adoption of computational methods for efficient data exploration. Topic modeling has emerged as a powerful tool for analyzing large-scale textual datasets, enabling the extraction of latent semantic structures. However, existing methods for topic modeling often struggle to provide interpretable representations that facilitate deeper insights into data structure and content.},
  address = {Cham},
  author = {Gutekunst, Klara M. and D{ü}rrschnabel, Dominik and Hirth, Johannes and Stumme, Gerd},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cellier, Peggy and Ganter, Bernhard and Missaoui, Rokia},
  keywords = {topic_aggregation},
  pages = {3--18},
  publisher = {Springer Nature Switzerland},
  title = {Conceptual Topic Aggregation},
  year = 2025
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-032-03364-2_1
  %A Gutekunst, Klara M.
  %A D{ü}rrschnabel, Dominik
  %A Hirth, Johannes
  %A Stumme, Gerd
  %B Conceptual Knowledge Structures
  %C Cham
  %D 2025
  %E Cellier, Peggy
  %E Ganter, Bernhard
  %E Missaoui, Rokia
  %I Springer Nature Switzerland
  %P 3--18
  %T Conceptual Topic Aggregation
  %X The vast growth of data has rendered traditional manual inspection infeasible, necessitating the adoption of computational methods for efficient data exploration. Topic modeling has emerged as a powerful tool for analyzing large-scale textual datasets, enabling the extraction of latent semantic structures. However, existing methods for topic modeling often struggle to provide interpretable representations that facilitate deeper insights into data structure and content.
  %@ 978-3-032-03364-2
  

• 1.

  Stubbemann, M., Hille, T., Hanika, T.: Unsupervised Selection of Features by Their Resilience to the Curse of Dimensionality. In: Braun, T., Paaßen, B., and Stolzenburg, F. (eds.) KI. pp. 161–174. Springer (2025).

  URLBibTeXEndNote

  @inproceedings{conf/ki/StubbemannHH25,
  author = {Stubbemann, Maximilian and Hille, Tobias and Hanika, Tom},
  booktitle = {KI},
  crossref = {conf/ki/2025},
  editor = {Braun, Tanya and Paaßen, Benjamin and Stolzenburg, Frieder},
  keywords = {itegpub},
  pages = {161-174},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Unsupervised Selection of Features by Their Resilience to the Curse of Dimensionality.},
  volume = 15956,
  year = 2025
  }
  

  %0 Conference Paper
  %1 conf/ki/StubbemannHH25
  %A Stubbemann, Maximilian
  %A Hille, Tobias
  %A Hanika, Tom
  %B KI
  %D 2025
  %E Braun, Tanya
  %E Paaßen, Benjamin
  %E Stolzenburg, Frieder
  %I Springer
  %P 161-174
  %T Unsupervised Selection of Features by Their Resilience to the Curse of Dimensionality.
  %U http://dblp.uni-trier.de/db/conf/ki/ki2025.html#StubbemannHH25
  %V 15956
  %@ 978-3-032-02813-6
  

• 1.

  de Portugal Mecke, D.C., Alyoussef, H., Stubbemann, M., Koloiarov, I., Hanika, T., Schmidt-Thieme, L.: STADE: Standard Deviation as a Pruning Metric, https://arxiv.org/abs/2503.22451, (2025).

  URLBibTeXEndNote

  @misc{mecke2025stadestandarddeviationpruning,
  author = {de Portugal Mecke, Diego Coello and Alyoussef, Haya and Stubbemann, Maximilian and Koloiarov, Ilia and Hanika, Tom and Schmidt-Thieme, Lars},
  keywords = {kde},
  title = {STADE: Standard Deviation as a Pruning Metric},
  year = 2025
  }
  

  %0 Generic
  %1 mecke2025stadestandarddeviationpruning
  %A de Portugal Mecke, Diego Coello
  %A Alyoussef, Haya
  %A Stubbemann, Maximilian
  %A Koloiarov, Ilia
  %A Hanika, Tom
  %A Schmidt-Thieme, Lars
  %D 2025
  %T STADE: Standard Deviation as a Pruning Metric
  %U https://arxiv.org/abs/2503.22451
  

• 1.

  Hille, T., Hanika, T.: Incomplete Formal Contexts and Their Intrinsic Dimension. In: Cellier, P., Ganter, B., and Missaoui, R. (eds.) SpringerLink. pp. 342–358. Springer, Cham, Switzerland (2025). https://doi.org/10.1007/978-3-032-03364-2_22.

  URLBibTeXEndNoteDOI

  @incollection{Hille2025Sep,
  address = {Cham, Switzerland},
  author = {Hille, Tobias and Hanika, Tom},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cellier, Peggy and Ganter, Bernhard and Missaoui, Rokia},
  journal = {SpringerLink},
  keywords = {itegpub},
  month = {09},
  pages = {342--358},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Incomplete Formal Contexts and Their Intrinsic Dimension},
  type = {Publication},
  volume = 15941,
  year = 2025
  }
  

  %0 Book Section
  %1 Hille2025Sep
  %A Hille, Tobias
  %A Hanika, Tom
  %B Conceptual Knowledge Structures
  %C Cham, Switzerland
  %D 2025
  %E Cellier, Peggy
  %E Ganter, Bernhard
  %E Missaoui, Rokia
  %I Springer
  %J SpringerLink
  %P 342--358
  %R 10.1007/978-3-032-03364-2_22
  %T Incomplete Formal Contexts and Their Intrinsic Dimension
  %U https://dblp.uni-trier.de/rec/conf/concepts/HilleH25.html
  %V 15941
  %@ 978-3-032-03364-2
  

• 1.

  Hanika, T., Jäschke, R.: A Repository for Formal Contexts. In: Proceedings of the 1st International Joint Conference on Conceptual Knowledge Structures (2024).

  URLBibTeXEndNote

  Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this.

  @inproceedings{hanika2024repository,
  abstract = {Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this.},
  author = {Hanika, Tom and Jäschke, Robert},
  booktitle = {Proceedings of the 1st International Joint Conference on Conceptual Knowledge Structures},
  keywords = {repository},
  title = {A Repository for Formal Contexts},
  year = 2024
  }
  

  %0 Conference Paper
  %1 hanika2024repository
  %A Hanika, Tom
  %A Jäschke, Robert
  %B Proceedings of the 1st International Joint Conference on Conceptual Knowledge Structures
  %D 2024
  %T A Repository for Formal Contexts
  %U https://arxiv.org/abs/2404.04344
  %X Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this.
  

• 1.

  Horn, V., Hirth, J., Holfeld, J., Behmenburg, J.H., Draude, C., Stumme, G.: Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content. In: Nordic Conference on Human-Computer Interaction. Association for Computing Machinery, Uppsala, Sweden (2024). https://doi.org/10.1145/3679318.3685414.

  URLBibTeXEndNoteDOI

  Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user’s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user’s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.

  @inproceedings{hci-lattice,
  abstract = {Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user’s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user’s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.},
  address = {New York, NY, USA},
  author = {Horn, Viktoria and Hirth, Johannes and Holfeld, Julian and Behmenburg, Jens Hendrik and Draude, Claude and Stumme, Gerd},
  booktitle = {Nordic Conference on Human-Computer Interaction},
  keywords = {itegpub},
  publisher = {Association for Computing Machinery},
  series = {NordiCHI 2024},
  title = {Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content},
  year = 2024
  }
  

  %0 Conference Paper
  %1 hci-lattice
  %A Horn, Viktoria
  %A Hirth, Johannes
  %A Holfeld, Julian
  %A Behmenburg, Jens Hendrik
  %A Draude, Claude
  %A Stumme, Gerd
  %B Nordic Conference on Human-Computer Interaction
  %C New York, NY, USA
  %D 2024
  %I Association for Computing Machinery
  %R 10.1145/3679318.3685414
  %T Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content
  %U https://doi.org/10.1145/3679318.3685414
  %X Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user’s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user’s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.
  %@ 9798400709661
  

• 1.

  Hirth, J.: Conceptual Data Scaling in Machine Learning, (2024). https://doi.org/10.17170/kobra-2024100910940.

  BibTeXEndNoteDOI

  Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.

  @phdthesis{doi:10.17170/kobra-2024100910940,
  abstract = {Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.},
  author = {Hirth, Johannes},
  keywords = {Knowldege~Representation},
  school = {Kassel, Universität Kassel, Fachbereich Elektrotechnik/Informatik},
  title = {Conceptual Data Scaling in Machine Learning},
  year = 2024
  }
  

  %0 Thesis
  %1 doi:10.17170/kobra-2024100910940
  %A Hirth, Johannes
  %D 2024
  %R 10.17170/kobra-2024100910940
  %T Conceptual Data Scaling in Machine Learning
  %X Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.
  

• 1.

  Hanika, T., Hille, T.: What is the intrinsic dimension of your binary data? -- and how to compute it quickly. In: CONCEPTS. pp. 97–112. Springer (2024).

  URLBibTeXEndNote

  @inproceedings{hanika2024intrinsic,
  author = {Hanika, Tom and Hille, Tobias},
  booktitle = {CONCEPTS},
  keywords = {itegpub},
  pages = {97--112},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {What is the intrinsic dimension of your binary data? -- and how to compute it quickly},
  volume = 14914,
  year = 2024
  }
  

  %0 Conference Paper
  %1 hanika2024intrinsic
  %A Hanika, Tom
  %A Hille, Tobias
  %B CONCEPTS
  %D 2024
  %I Springer
  %P 97--112
  %T What is the intrinsic dimension of your binary data? -- and how to compute it quickly
  %U http://dblp.uni-trier.de/db/conf/concepts/concepts2024.html#HanikaH24
  %V 14914
  %@ 978-3-031-67868-4
  

• 1.

  Hirth, J., Hanika, T.: The Geometric Structure of Topic Models, (2024).

  BibTeXEndNote

  Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.

  @preprint{hirth2024geometric,
  abstract = {Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.},
  author = {Hirth, Johannes and Hanika, Tom},
  keywords = {kde},
  title = {The Geometric Structure of Topic Models},
  year = 2024
  }
  

  %0 Generic
  %1 hirth2024geometric
  %A Hirth, Johannes
  %A Hanika, Tom
  %D 2024
  %T The Geometric Structure of Topic Models
  %X Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.
  

• 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.

  URLBibTeXEndNoteDOI

  @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. In: Cabrera, I.P., Ferré, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 20–35. Springer Nature Switzerland, Cham (2024).

  BibTeXEndNote

  We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.

  @inproceedings{10.1007/978-3-031-67868-4_2,
  abstract = {We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.},
  address = {Cham},
  author = {Abdulla, Mohammad and Hirth, Johannes and Stumme, Gerd},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cabrera, Inma P. and Ferré, Sébastien and Obiedkov, Sergei},
  keywords = {itegpub},
  pages = {20--35},
  publisher = {Springer Nature Switzerland},
  title = {The Birkhoff Completion of Finite Lattices},
  year = 2024
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-67868-4_2
  %A Abdulla, Mohammad
  %A Hirth, Johannes
  %A Stumme, Gerd
  %B Conceptual Knowledge Structures
  %C Cham
  %D 2024
  %E Cabrera, Inma P.
  %E Ferré, Sébastien
  %E Obiedkov, Sergei
  %I Springer Nature Switzerland
  %P 20--35
  %T The Birkhoff Completion of Finite Lattices
  %X We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.
  %@ 978-3-031-67868-4
  

• 1.

  Draude, C., Dürrschnabel, D., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M.: Conceptual Mapping of Controversies. In: Cabrera, I.P., Ferré, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 201–216. Springer Nature Switzerland, Cham (2024).

  BibTeXEndNote

  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.

  @inproceedings{10.1007/978-3-031-67868-4_14,
  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.},
  address = {Cham},
  author = {Draude, Claude and Dürrschnabel, Dominik and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, J{ö}rn and Stumme, Gerd and Uhlmann, Markus},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cabrera, Inma P. and Ferré, Sébastien and Obiedkov, Sergei},
  keywords = {itegpub},
  pages = {201--216},
  publisher = {Springer Nature Switzerland},
  title = {Conceptual Mapping of Controversies},
  year = 2024
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-67868-4_14
  %A Draude, Claude
  %A Dürrschnabel, Dominik
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Kropf, Jonathan
  %A Lamla, J{ö}rn
  %A Stumme, Gerd
  %A Uhlmann, Markus
  %B Conceptual Knowledge Structures
  %C Cham
  %D 2024
  %E Cabrera, Inma P.
  %E Ferré, Sébastien
  %E Obiedkov, Sergei
  %I Springer Nature Switzerland
  %P 201--216
  %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.
  %@ 978-3-031-67868-4
  

• 1.

  Hille, T., Stubbemann, M., Hanika, T.: Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research, (2024).

  BibTeXEndNote

  @preprint{hille2024reproducibility,
  author = {Hille, Tobias and Stubbemann, Maximilian and Hanika, Tom},
  keywords = {intrinsic},
  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.

  Draude, C., D{ü}rrschnabel, D., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M.: Conceptual Mapping of Controversies. In: Cabrera, I.P., Ferr{é}, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 201–216. Springer Nature Switzerland, Cham (2024).

  BibTeXEndNote

  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.

  @inproceedings{10.1007/978-3-031-67868-4_14,
  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.},
  address = {Cham},
  author = {Draude, Claude and D{ü}rrschnabel, Dominik and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, J{ö}rn and Stumme, Gerd and Uhlmann, Markus},
  booktitle = {Conceptual Knowledge Structures},
  editor = {Cabrera, Inma P. and Ferr{é}, S{é}bastien and Obiedkov, Sergei},
  keywords = {formal_concept_analysis},
  pages = {201--216},
  publisher = {Springer Nature Switzerland},
  title = {Conceptual Mapping of Controversies},
  year = 2024
  }
  

  %0 Conference Paper
  %1 10.1007/978-3-031-67868-4_14
  %A Draude, Claude
  %A D{ü}rrschnabel, Dominik
  %A Hirth, Johannes
  %A Horn, Viktoria
  %A Kropf, Jonathan
  %A Lamla, J{ö}rn
  %A Stumme, Gerd
  %A Uhlmann, Markus
  %B Conceptual Knowledge Structures
  %C Cham
  %D 2024
  %E Cabrera, Inma P.
  %E Ferr{é}, S{é}bastien
  %E Obiedkov, Sergei
  %I Springer Nature Switzerland
  %P 201--216
  %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.
  %@ 978-3-031-67868-4
  

• 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.

  URLBibTeXEndNoteDOI

  @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.

  Dürrschnabel, D., Priss, U.: Realizability of Rectangular Euler Diagrams, (2024).

  BibTeXEndNote

  @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