AI RESEARCH
Bounded Fitting for Expressive Description Logics
arXiv CS.AI
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ArXi:2605.07452v1 Announce Type: new 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.