Acquiring and maintaining Semantic Web rules is very demanding and can be automated though partially by applying Machine Learning algorithms. In this paper we show that the form of Machine Learning known under the name of Inductive Logic Programming (ILP) can help. In particular, we take a critical look at two ILP proposals based on knowledge representation frameworks that integrate Description Logics and Horn Clausal Logic and draw from them general conclusions that can be considered as guidelines for further ILP research of interest to the Semantic Web.

Building Rules on top of Ontologies? Inductive Logic Programming can help! / LISI F.A.; ESPOSITO F.. - 314(2007), pp. 120-129. ((Intervento presentato al convegno 4th Italian Workshop on Semantic Web Applications and Perspectives, SWAP 2007 tenutosi a Bari, Italy nel 18-20 December 2007.

Building Rules on top of Ontologies? Inductive Logic Programming can help!

LISI, Francesca Alessandra;ESPOSITO, Floriana
2007

Abstract

Acquiring and maintaining Semantic Web rules is very demanding and can be automated though partially by applying Machine Learning algorithms. In this paper we show that the form of Machine Learning known under the name of Inductive Logic Programming (ILP) can help. In particular, we take a critical look at two ILP proposals based on knowledge representation frameworks that integrate Description Logics and Horn Clausal Logic and draw from them general conclusions that can be considered as guidelines for further ILP research of interest to the Semantic Web.
978-88-902981-1-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/116673
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