Abstract
The main goal of information retrieval is getting the most relevant documents to a user's query. So, a search engine must not only understand the meaning of each keyword in the query but also their relative senses in the context of the query. Discovering the query meaning is a comprehensive and evolutionary process; the precise meaning of the query is established as developing the association between concepts. The meaning determination process is modeled by a dynamic system operating in the semantic space of WordNet. To capture the meaning of a user query, the original query is reformulated into candidate queries by combining the concepts and their synonyms. A semantic score characterizing the overall meaning of such queries is calculated, and the one with the highest score was used to perform the search. The results confirm that the proposed "query sense discovery" approach provides a significant improvement in several performance measures.