Proceedings

Bounded Fitting for Expressive Description Logics

Maurice Funk, Jean Christoph Jung, Tom Voellmer

Download BibTeX Cite

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 3855-3863.

https://doi.org/10.24963/ijcai.2026/429

Keywords

  • Machine Learning: Supervised Learning
  • Machine Learning: Learning theory
  • Knowledge Representation and Reasoning: Learning and reasoning
  • Knowledge Representation and Reasoning: Description logics and ontologies

Abstract

Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and can often be implemented leveraging SAT solvers. It has been successfully applied to learning concepts of the description logic ALC. We study bounded fitting for learning concepts in expressive description logics that extend ALC with inverse roles, qualified number restrictions, and feature comparisons. We investigate under which conditions bounded fitting keeps its favorable theoretical properties in this setting, and implement is using a SAT solver. We compare our implementation against state-of-the-art concept learners with encouraging results, demonstrating that it is a practical approach to expressive concept learning.

Maurice Funk, Jean Christoph Jung, and Tom Voellmer. 2026. Bounded Fitting for Expressive Description Logics. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 3855–3863. https://doi.org/10.24963/ijcai.2026/429
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards