Towards learning fuzzy DL inclusion axioms
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
A Logic-based Computational Method for the Automated Induction of Fuzzy Ontology Axioms
Fundamenta Informaticae - Special Issue on the Italian Conference on Computational Logic: CILC 2011
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Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability when learning classical rules or getting more expressivity by learning fuzzy rules. This latter concern is the topic of this paper. Indeed, introducing fuzzy predicates in the antecedent and in the consequent of rules may convey different non-classical meanings. The paper focuses on the learning of gradual and certainty rules, which have an increased expressive power and have no simple crisp counterpart. The benefit and the application domain of each kind of rules are discussed. Appropriate confidence degrees for each type of rules are introduced. These confidence degrees play a major role in the adaptation of the classical FOIL inductive logic programming algorithm to the induction of fuzzy rules for guiding the learning process. The method is illustrated on a benchmark example and a case-study database.