Five members of the Foundations of Knowledge Representation research group from Leipzig University – Marvin Großer, Simon Hosemann, Quentin Manière, Moritz Schönherr and Lukas Schulze – together with Matti Berthold claimed a double victory: they won the ‘Best Video Award’ (Jury) and the ‘People's Choice Video Award’ for their video titled „Waiting for the Chase to Terminate: Have you tried this other variant?”

The video introduces the chase algorithm, which is a fundamental technique in database theory and knowledge representation. The basic idea is that, given an incomplete database and background knowledge in the form of logical rules (known as existential rules), the chase algorithm completes the database by successively applying these rules. Ideally, this allows queries to be answered definitively, depending on the database and the background knowledge. However, in some cases, the application of the rules can occur in different ways or continue indefinitely, raising questions about termination and computational complexity. The video explains the different variants of the chase algorithm and the trade-offs between them. To make the topic more accessible, the video employs a narrative framework in which a magic shop owner and his apprentice use the chase algorithm to brew potions for their customers. To simplify the subject, it uses graphical representations and animations instead of complex mathematical notation.

About the research group:

The Foundations of Knowledge Representation group at Leipzig University is led by Prof. Dr. Carsten Lutz. The group conducts research on logic-based knowledge representation, particularly description logics, and theoretical aspects of databases, with a focus on expressiveness and computational complexity. Its members work on topics ranging from ontology learning and query answering to the logical characterization of graph neural networks.

About Simon Hosemann:

Simon Hosemann is a doctoral student in the Foundations of Knowledge Representation group at Leipzig University, supervised by the fellows Carsten Lutz and Sebastian Rudolph, and a member of the SECAI Graduate School since December 2022. His research lies at the intersection of knowledge representation and logic in computer science. He explores fundamental questions about the relationship between data and formal knowledge representation. In particular, he studies how ontologies formulated in description logics and logical constraints such as existential rules can be learned from, characterized by, and fitted to data examples, and analyses the computational complexity of these tasks. This line of research contributes to making knowledge-based systems more accessible, by investigating how ontology engineers can construct ontologies and their building blocks from examples, rather than specifying them fully manually. The recent joint paper at KR 2025 addresses one such question by studying the complexity and feasibility of constructing ontologies and constraints from positive and negative examples given as finite relational structures.