Abstract: Searching and finding out useful information from a mass stock in a very short time is becoming a tough tax and many times the user will have to receive a lot of information that may not appear any useful for the users.Problems mainly come from the query which users have provided without enough accuracy,the mismatches of the queries and the expression of the documents,or query optimization.To deal with these problems,this paper proposed a novel hybrid query expansion method which synthesizes the merits of semantic query expansion and local context analysis(LCA).Firstly,we retrieved the documents by LCA method,then used these terms to trim the semantic tree,and calculated the weight of expansion term based on this improved algorithm.We compared the effectiveness of these approaches.And the results show that,although local context analysis has some advantages,the LCA prune semantic tree yields better performance than the techniques on the simple query expansion.

Key words: Query expansion,Local context analysis,Concept tree,Pruning,Relevance algorithm