Learning SHIQ+log Rules for Ontology Evolution

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.

• Type: Conference
• Year: 2008
• Where: SWAP
• Authors: Francesca A. Lisi, Floriana Esposito
• Keywords: DL Ontology Evolve, Internet Technology, Machine Learning, Machine Learning Problem, SWAP 2008