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A FOIL-like method for learning under incompleteness and vagueness

Chapter
Publication Date:
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.
Iris type:
02.01 Contributo in volume (Capitolo o Saggio)
Keywords:
OWL; Semantic Web; Learning; Fuzzy Sets
List of contributors:
Straccia, Umberto
Authors of the University:
STRACCIA UMBERTO
Handle:
https://iris.cnr.it/handle/20.500.14243/222907
Book title:
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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