based based Semantic and Personalized Information Retrieval Semantic and Personalized Information Retrieval Semantic and Personalized Information Retrieval Semantic and Personalized Information Retrieval

Effective retrieval of the most relevant documents on the top ic of interest from the Web is difficult due to the large amount of information in all types of formats. Studies have been conducted on ways to improve the efficiency of information retrieval (IR) systems. To arrive to suitable solutions in IR systems, machines need additional semantic information that helps in understanding Web documents. In this paper, the semantic IR model is investigated, oriented to the exploitation of domain ontology and WordNet to support semantic IR capabilities in Web documents, stressing on the use of ontologies in the semantic-based perspective. The system; called SPIRS, that uses Semantic Web and agent to support more expressive queries and more accurate results is proposed. The examination of the proposed system is performed by an experiment that is based on relevance based evaluation and user satisfaction based evaluation. The results of the experiment shows that the proposed system, which is based on Semantic Web and agent, can improve the accuracy and effectiveness for retrieving relevant Web documents in specific domain.

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