Intent based clustering of search engine query log

The keyword based search technique suffers from the problem of synonymic and polysemic queries. Current approaches address only the problem of synonymic queries in which different queries might have the same information requirement. But the problem of polysemic queries, i.e., same query having different intentions, still remains unaddressed. In this paper, we propose the notion of intent clusters, the members of which will have the same intention. We develop a clustering algorithm that uses the user session information in query logs in addition to query URL entries to identify cluster of queries having the same intention. The proposed approach has been studied through case examples from the actual log data from AOL, and the clustering algorithm is shown to be successful in discerning the user intentions.