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A FOIL-like method for learning under incompleteness and vagueness

Capitolo di libro
Data di Pubblicazione:
2014
Abstract:
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.
Tipologia CRIS:
02.01 Contributo in volume (Capitolo o Saggio)
Keywords:
OWL; Semantic Web; Learning; Fuzzy Sets
Elenco autori:
Straccia, Umberto
Autori di Ateneo:
STRACCIA UMBERTO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/222907
Titolo del libro:
Inductive Logic Programming
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URL

http://link.springer.com/chapter/10.1007%2F978-3-662-44923-3_9
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