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Istituto di Scienza e Tecnologie dell'Informazione     
Lisi A. F., Straccia U. A FOIL-like method for learning under incompleteness and vagueness. Gerson Zaverucha, Vítor Santos Costa, Aline Paes (eds.). (Lecture Notes in Artificial Intelligence, vol. 8812). Heidelberg: Springer, 2014.
 
 
Abstract
(English)
Incompleteness and vagueness are inherent properties of knowledge in several real world domains and are particularly pervading in those domains where entities could be better described in natural language. In order to deal with incomplete and vague structured knowledge, several fuzzy extensions of Description Logics (DLs) have been proposed in the literature. In this paper, we present a novel Foil-like method for inducing fuzzy DL inclusion axioms from crisp DL knowledge bases and discuss the results obtained on a real-world case study in the tourism application domain also in comparison with related works.
URL: http://link.springer.com/chapter/10.1007%2F978-3-662-44923-3_9
DOI: 10.1007/978-3-662-44923-3_9
Subject OWL
Semantic Web
Learning
Fuzzy Sets
I.2.4 Knowledge Representation Formalisms and Methods


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