University of Kassel
Knowledge & Data Engineering Group

Knowledge & Data Engineering Group (KDE), EECS, University of Kassel

The research unit Knowledge & Data Engineering at the Department of Electrical Engineering/Computer Science is developing methods for knowledge discovery and representation (approximation and exploration of knowledge, order structures in knowledge, ontology learning) and for the analysis of (social) networks and related knowledge processes (metrics in networks, anomaly detection, characterization of social networks). Our focus is on the exact algebraic modelling of structures in knowledge and networks. Our research on foundations in order and lattice theory, description logics, graph theory and ontologies is complemented by applications in social media and scientometrics. The research unit Knowledge & Data Engineering is member in the Interdisciplinary Research Center for Information System Design (ITeG) and the International Centre for Higher Education Research (INCHER Kassel) at the University of Kassel and in the Hessian Center for Artificial Intelligence (hessian.AI).

Our latest publications

  • 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.
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    Abdulla, M., Hirth, J., Stumme, G.: The Birkhoff Completion of Finite Lattices. In: Cabrera, I.P., Ferr{é}, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 20–35. Springer Nature Switzerland, Cham (2024).
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    Dürrschnabel, D., Priss, U.: Realizability of Rectangular Euler Diagrams, (2024).
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    Draude, C., Dürrschnabel, D., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M.: Conceptual Mapping of Controversies, (2024).
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    Hirth, J., Hanika, T.: The Geometric Structure of Topic Models, (2024). https://doi.org/10.48550/arxiv.2403.03607.
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    Hille, T., Stubbemann, M., Hanika, T.: Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research, (2024).
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    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).
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    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.
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    Hanika, T., Jäschke, R.: A Repository for Formal Contexts. In: Proceedings of the 1st International Joint Conference on Conceptual Knowledge Structures (2024).
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    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.
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    Hirth, J.: Conceptual Data Scaling in Machine Learning, (2024). https://doi.org/10.17170/kobra-2024100910940.
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    Hanika, T., Hille, T.: What is the intrinsic dimension of your binary data? -- and how to compute it quickly, (2024).
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    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.
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    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.
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    Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal Motifs in Lattices, https://arxiv.org/abs/2304.04827, (2023).
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    Stubbemann, M., Hanika, T., Schneider, F.M.: Intrinsic Dimension for Large-Scale Geometric Learning. Transactions on Machine Learning Research. (2023).
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    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.
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    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.
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    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.
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    Koyda, M., Stumme, G.: Factorizing Lattices by Interval Relations. Int. J. Approx. Reason. 157, 70–87 (2023).
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    Stubbemann, M., Hille, T., Hanika, T.: Selecting Features by their Resilience to the Curse of Dimensionality. (2023).
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