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The exponential growth of the data available on the Web requires more efficient search tools to find relevant information. In the context of the Semantic Web, ontologies improve the exploitation of Web resources by adding a consensual knowledge. However, the automation of ontology building is a hard problem. The exploitation of the users’ search feedback can aid to address that problem. In order to apply this solution, we present a semantic search system based on case-based-reasoning (CBR) and ontologies that automatically enriches the ontologies by using previous queries. In this paper we expose the contribution of CBR and ontologies in Semantic Web search. Some experiments on the system are presented, and the obtained results show an improvement on the precision of the Web search and ontology enrichment.

EVOLUTIVE CONTENT-BASED SEARCH SYSTEM - Semantic Search System based on Case-based-Reasoning and Ontology Enrichment