A model based agents and ontology for semantic information search

The vector space model and various statistic/probabilistic approaches are widely used in models of information search for representing documents and user requests. The documents recovered are relatively relevant and generally troubled by noise and silence. Our work is to propose a model whose objective is to improve the results towards a user information need, this will be done by acting on measures of precision and recall, for this, first we use a multi agents system to reproduce the concepts of autonomy, cooperation and communication which are inherent to this type of search systems, and secondly our approach will combine a syntactic search improved by the use of semantics that provides the WordNet ontology, with a search engine based domain ontology. The knowledge base represented by the domain ontology is used to annotate documents by the concepts and the defined instances, therefore these form equivalent classes to classic indexing and constitute the semantic index on which the proposed search model is based.