Learning SHIQ+log Rules for Ontology Evolution

Francesca A. Lisi, Floriana Esposito
2008 Semantic Web Applications and Perspectives  
The definition of new concepts or roles for which extensional knowledge become available can turn out to be necessary to make a DL ontology evolve. In this paper we reformulate this task as a machine learning problem and study a solution based on techniques borrowed from that form of logic-based machine learning known under the name of Inductive Logic Programming (ILP). More precisely, we propose to adapt previous ILP results to the knowledge representation framework of DL+log in order to learn rules to be used for changing SHIQ ontologies.
dblp:conf/swap/LisiE08 fatcat:wjnbiuqqnzgpvfp42acp2llfai