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, in the L3S Research Center and in the Hessian Center for Artificial Intelligence (hessian.AI).

Our latest publications

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    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://www.forum-privatheit.de/wp-content/uploads/Forschungsberichte-1-2024-WP-Fairdienste.pdf, (2024). https://doi.org/10.24406/publica-2497.
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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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    Dürrschnabel, D., Stumme, G.: Greedy Discovery of Ordinal Factors, http://arxiv.org/abs/2302.11554, (2023).
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    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). https://doi.org/10.1007/978-3-031-35949-1\_5.
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    Budde, K.B., Rellstab, C., Heuertz, M., Gugerli, F., Hanika, T., Verdú, M., Pausas, J.G., González-Martínez, S.C.: Divergent selection in a Mediterranean pine on local spatial scales. Journal of Ecology. n/a, (2023). https://doi.org/https://doi.org/10.1111/1365-2745.14231.
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    Stumme, G., Dürrschnabel, D., Hanika, T.: Towards Ordinal Data Science, (2023).
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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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    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.
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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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    Stubbemann, M., Hille, T., Hanika, T.: Selecting Features by their Resilience to the Curse of Dimensionality. (2023).
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    Dürrschnabel, D., Stumme, G.: Maximal Ordinal Two-Factorizations. In: Ojeda-Aciego, M., Sauerwald, K., and Jäschke, R. (eds.) Graph-Based Representation and Reasoning. pp. 41–55. Springer Nature Switzerland, Cham (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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    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.
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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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    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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    Hanika, T., Hirth, J.: Conceptual views on tree ensemble classifiers. International Journal of Approximate Reasoning. 159, 108930 (2023). https://doi.org/https://doi.org/10.1016/j.ijar.2023.108930.
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