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.

Learning SHIQ+log Rules for Ontology Evolution / LISI F.A.; ESPOSITO F.. - 426(2008). ((Intervento presentato al convegno SWAP 2008 tenutosi a Rome Italy nel December 15-17.

Learning SHIQ+log Rules for Ontology Evolution

LISI, Francesca Alessandra;ESPOSITO, Floriana
2008

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/116239
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