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 and in theL3S Research Center.

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Our latest publications:

  • 1.
    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., Stumme, G.: Interactive collaborative exploration using incomplete contexts. Data & Knowledge Engineering. 143, 102104 (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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    Hanika, T., Hirth, J.: Knowledge cores in large formal contexts. Annals of Mathematics and Artificial Intelligence. (2022).
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    Dürrschnabel, 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 (2022).
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    Hanika, T., Hirth, J.: On the lattice of conceptual measurements. Information Sciences. 613, 453–468 (2022).
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    Hirth, J., Hanika, T.: Formal Conceptual Views in Neural Networks, http://arxiv.org/abs/2209.13517, (2022).
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    Stubbemann, M., Hanika, T., Schneider, F.M.: Intrinsic Dimension for Large-Scale Geometric Learning, https://arxiv.org/abs/2210.05301, (2022).
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    Felde, M., Koyda, M.: Interval-Dismantling for Lattices, https://arxiv.org/abs/2208.01479, (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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    Stubbemann, M., Stumme, G.: LG4AV: Combining Language Models and Graph Neural Networks for Author Verification. In: Bouadi, T., Fromont, E., and Hüllermeier, E. (eds.) Advances in Intelligent Data Analysis XX. pp. 315–326. Springer International Publishing, Cham (2022).
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    Felde, M., Stumme, G.: Attribute Exploration with Multiple Contradicting Partial Experts. In: Braun, T., Cristea, D., and Jäschke, R. (eds.) Graph-Based Representation and Reasoning. pp. 51–65. 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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    Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic dimension of geometric data sets. Tohoku Mathematical Journal. 74, 23–52 (2022).
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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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    Schaefermeier, B., Stumme, G., Hanika, T.: Topological Indoor Mapping through WiFi Signals. (2021).
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    Schaefermeier, B., Stumme, G., Hanika, T.: Topic space trajectories. Scientometrics. 126, 5759–5795 (2021).
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    Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G.: Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research. Scientometrics. (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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