Patrick Betz

Researcher

University of Mannheim
B6, 26
D-68159 Mannheim

Email: patrick (at) informatik.uni-mannheim.de

Research group:  Artificial Intelligence

• Neuro-Symbolic AI
• Knowledge representations

PyClause - A library for symbolic rule handling for knowledge graphs

• P. Betz, L. Galarraga, S. Ott, C. Meilicke, F. Suchanek,  H. Stuckenschmidt
  PyClause – Simple and Efficient Rule Handling for Knowledge Graphs
  In: IJCAI (demo track, forthcoming), 2024
• S. Ott, P. Betz, D. Stepanova, M. H. Gad-Elrab, C. Meilicke, H. Stuckenschmidt
  Rule-based knowledge graph completion with canonical models
  In: CIKM, 2023
• P. Betz,  S. Lüdtke, C. Meilicke, H. Stuckenschmidt
  On the aggregation of rules for knowledge graph completion
  In: Knowledge and Logical Reasoning in the Era of Data-driven Learning Workshop@ICML 2023
• C. Meilicke, M. W. Chekol, P. Betz, M. Fink, H Stuckenschmidt.
  Anytime bottom-up rule learning for large scale knowledge graph completion
  In: The VLDB Journal, 2023
• P. Betz, C. Meilicke, H. Stuckenschmidt
  Adversarial explanations for knowledge graph embeddings
  In: IJCAI, 2022
• P. Betz, C. Meilicke, H. Stuckenschmidt
  Supervised knowledge aggregation for knowledge graph completion
  In: ESWC, 2022
• P. Betz, M. Niepert, P. Minervini, H. Stuckenschmidt
  Backpropagating through markov logic networks
  In: Proceedings of 15th International Workshop on Neural-Symbolic Learning and Reasoning, 2021
• C. Meilicke, P. Betz, H. Stuckenschmidt
  Why a naive way to combine latent and symbolic knowledge base completion works surprisingly well
  In: 3rd Conference on Automated Knowledge Base Construction, 2021
• S. Broscheit, D. Ruffinelli, A. Kochsiek, P. Betz, R. Gemulla
  LibKGE – A knowledge graph embedding library for reproducible research
  In: EMNLP (demo), 2020