Approximate Classification of Semantically Annotated Web Resources Exploiting Pseudo-metrics Induced by Local Models

Authors:
Claudia d'Amato;Nicola Fanizzi;Floriana Esposito;Thomas Lukasiewicz
Affiliations:
-;-;-;-
Venue:
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Year:
2009

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Abstract

We present a classification method, founded in the instance-based learning and the disjunctive version space approach, for performing approximate retrieval from knowledge bases expressed in Description Logics. The method supplies answers even if the knowledge base of reference is inconsistent or incomplete. Moreover, the method may also induce new knowledge that can be suggested to the knowledge engineer, thus making the ontology population task semi-automatic.