Professorship for Information Systems, in particular Artificial Intelligence and Explainability

Organisational unit: Professoship

Organisation profile

The research group on Artificial Intelligence and Explainability focuses on hybrid AI methods to make machines understand human-created data. That is, we combine modern, sub-symbolic machine learning approaches with symbolic knowledge engineering based on Knowledge Graphs to develop transparent AI systems. We focus on Knowledge Extraction, Speech Assistants, Question Answering, AI ethics, and more.

  1. Published

    Bridge-Generate: Scholarly Hybrid Question Answering

    Taffa, T. A. & Usbeck, R., 2025, WWW Companion’25: Companion Proceedings of the ACM Web Conference 2025, April 28-May 2, 2025 Sydney, NSW, Australia. Long, G., Blumestein, M., Chang, Y., Lewin-Eytan, L., Huang, H. & Yom-Tov, E. (eds.). New York: Association for Computing Machinery, Inc, p. 1321-1325 5 p.

    Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

  2. Published

    Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering

    Jiang, L., Huang, J., Möller, C. & Usbeck, R., 06.02.2025

    Research output: other publicationsOtherResearch

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