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 theInterdisciplinary Research Center for Information System Design (ITeG) and the International Centre for Higher Education Research (INCHER Kassel) at the University of Kassel, in theL3S Research Center and in the Hessian Center for Artificial Intelligence (hessian.AI).

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Our latest publications

  • Ganter, B., Hanika, T., Hirth, J.: Scaling Dimension, https://arxiv.org/abs/2302.09101, (2023).
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  • Hirth, J., Horn, V., Stumme, G., Hanika, T.: Automatic Textual Explanations of Concept Lattices, http://arxiv.org/abs/2304.08093, (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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  • Hanika, T., Hirth, J.: Conceptual Views on Tree Ensemble Classifiers CoRR. abs/2302.05270, (2023).
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  • Felde, M., Stumme, G.: Interactive collaborative exploration using incomplete contexts Data & Knowledge Engineering. 143, 102104 (2023).
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  • Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal Motifs in Lattices, http://arxiv.org/abs/2304.04827, (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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  • Dürrschnabel, D., Stumme, G.: Greedy Discovery of Ordinal Factors, http://arxiv.org/abs/2302.11554, (2023).
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  • Dürrschnabel, D., Stumme, G.: Maximal Ordinal Two-Factorizations, http://arxiv.org/abs/2304.03338, (2023).
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  • Schäfermeier, B., Stumme, G., Hanika, T.: Mapping Research Trajectories, https://arxiv.org/abs/2204.11859, (2022).
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  • Stubbemann, M., Stumme, G.: LG4AV: Combining Language Models and Graph Neural Networks for Author Verification In: Bouadi, T., Fromont, E., and H{ü}llermeier, E. (eds.) Advances in Intelligent Data Analysis XX. pp. 315–326. Springer International Publishing, Cham (2022).
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  • D{ü}rrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis In: Missaoui, R., Kwuida, L., and Abdessalem, T. (eds.) Complex Data Analytics with Formal Concept Analysis. pp. 47–74. Springer International Publishing, Cham (2022).
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  • Dürrschnabel, D., Hanika, T., Stumme, G.: Discovering Locally Maximal Bipartite Subgraphs, http://arxiv.org/abs/2211.10446, (2022).
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  • Felde, M., Stumme, G.: Attribute Exploration with Multiple Contradicting Partial Experts In: Braun, T., Cristea, D., and J{ä}schke, R. (eds.) Graph-Based Representation and Reasoning. pp. 51–65. Springer International Publishing, Cham (2022).
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  • Schäfermeier, B., Hirth, J., Hanika, T.: Research Topic Flows in Co-Authorship Networks, https://doi.org/10.1007/s11192-022-04529-w, (2022).
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  • Felde, M., Koyda, M.: Interval-Dismantling for Lattices, https://arxiv.org/abs/2208.01479, (2022).
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  • Hanika, T., Schneider, F.M., Stumme, G.: {Intrinsic dimension of geometric data sets} Tohoku Mathematical Journal. 74, 23–52 (2022).
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  • Schaefermeier, B., Stumme, G., Hanika, T.: Topic space trajectories Scientometrics. 126, 5759–5795 (2021).
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  • Schaefermeier, B., Stumme, G., Hanika, T.: Topological Indoor Mapping through WiFi Signals (2021).
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  • Schäfermeier, B., Stumme, G., Hanika, T.: Towards Explainable Scientific Venue Recommendations, http://arxiv.org/abs/2109.11343, (2021).
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  • Schäfermeier, B., Hanika, T., Stumme, G.: Distances for WiFi Based Topological Indoor Mapping 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), November 12--14, 2019, Houston, TX, USA (2019).
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