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The size of publicly indexable World Wide Web has probably surpassed 14.3 billion documents and as yet growth shows no sign of leveling off. As more information becomes available on the Web it is more difficult to provide effective search services for Internet users. Since, it is assumed that users do not always formulate search queries using the best terms. So, search engines invoke query expansion to increase the quality of user search results. Query expansion is useful in reducing this query/document mismatch by expanding the query using words or phrases with a similar meaning or some other statistical relation to the set of relevant documents. This paper proposes an approach for ontology driven conjunctive query expansion based on mining user logs. In this, the clustered indexed documents and information extracted from user log are mined. Also, the association rules and ontology have been used for inferring rules and for identifying the relationship between the concepts. Further, an algorithm has been proposed and the performance has been measured.