Using query log and social tagging to refine queries based on latent topics

An important way to improve users' satisfaction in Web search is to assist them to issue more effective queries. One such approach is query refinement (reformulation), which generates new queries according to the current query issued by users. A common procedure for conducting refinement is to generate some candidate queries first, and then a scoring method is designed to assess the quality of these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the historical queries, and cannot detect and maintain the semantic consistency of queries. In this paper, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to assess the semantic dependency of terms in a query. In the graphical model, both term context dependency and topic context dependency are considered. This also makes it feasible to score some queries which do not have much available historical term context information. We also utilize social tagging data in the candidate query generation process. Based on the observation that different users may tag the same resource with different tags of similar meaning, we propose a method to mine these term pairs for new candidate query construction.