Mining web query hierarchies from clickthrough data

In this paper, we propose to mine query hierarchies from clickthrough data, which is within the larger area of automatic acquisition of knowledge from the Web. When a user submits a query to a search engine and clicks on the returned Web pages, the user's understanding of the query as well as its relation to the Web pages is encoded in the clickthrough data. With millions of queries being submitted to search engines every day, it is both important and beneficial to mine the knowledge hidden in the queries and their intended Web pages. We can use this information in various ways, such as providing query suggestions and organizing the queries. In this paper, we plan to exploit the knowledge hidden in clickthrough logs by constructing query hierarchies, which can reflect the relationship among queries. Our proposed method consists of two stages: generating candidate queries and determining "generalization/ specialization" relations between these queries in a hierarchy. We test our method on some labeled data sets and illustrate the effectiveness of our proposed solution empirically.