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A System for Learning GCI Axioms in Fuzzy Description Logics

Vagueness is inherent to several real world domains and is particularly pervading in those domains where entities could be better described in natural language. In order to deal with vague knowledge, several fuzzy extensions of DLs have been proposed. In this paper, we face the problem of supporting the evolution of DL ontologies under vagueness. Here, we present a system for learning fuzzy GCI axioms from crisp assertions and discuss preliminary experimental results obtained in the tourism application domain.