Learning in Description Logics with Fuzzy Concrete Domains

• Autorzy: Lisi F. A. , Straccia U.

Identyfikatory

DOI

10.3233/FI-2015-1259

• Konferencja: Italian Conference on Computational Logic, CILC 2013, (25-27.09.2013; Catania, Italy)
• Języki publikacji: EN

Abstrakty

EN

Description Logics (DLs) are a family of logic-based Knowledge Representation (KR) formalisms, which are particularly suitable for representing incomplete yet precise structured knowledge. Several fuzzy extensions of DLs have been proposed in the KR field in order to handle imprecise knowledge which is particularly pervading in those domains where entities could be better described in natural language. Among the many approaches to fuzzification in DLs, a simple yet interesting one involves the use of fuzzy concrete domains. In this paper, we present a method for learning within the KR framework of fuzzy DLs. The method induces fuzzy DL inclusion axioms from any crisp DL knowledge base. Notably, the induced axioms may contain fuzzy concepts automatically generated from numerical concrete domains during the learning process. We discuss the results obtained on a popular learning problem in comparison with state-of-the-art DL learning algorithms, and on a test bed in order to evaluate the classification performance.

Słowa kluczowe

EN

fuzzy description logics; ontologies; concept learning

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2015
• Tom: Vol. 140, nr 3/4
• Strony: 373--391
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Lisi F. A.

• Dipartimento di Informatica Universit`a degli Studi di Bari “Aldo Moro”, Italy

autor

Straccia U.

• ISTI - CNR, Pisa, Italy

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