Bounded Fitting for Expressive Description Logics

Maurice Funk; Jean Christoph Jung; Tom Voellmer

IJCAI 20260 citations

Bounded Fitting for Expressive Description Logics

Maurice Funk, Jean Christoph Jung, Tom Voellmer

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.

Knowledge Representation and Reasoning: Description logics and ontologiesKnowledge Representation and Reasoning: Learning and reasoningMachine Learning: Learning theoryMachine Learning: Supervised Learning

BibTeX

@inproceedings{ijcai2026_boundedfittingfo,
  title = {Bounded Fitting for Expressive Description Logics},
  author = {Maurice Funk and Jean Christoph Jung and Tom Voellmer},
  booktitle = {IJCAI 2026},
  year = {2026}
}