Abstract

Abstract. The human interaction through the web generates both im-plicit and explicit knowledge. An example of an implicit contribution is searching, as people contribute with their knowledge by clicking on re-trieved documents. Thus, an important and interesting challenge is to extract semantic relations among queries and their terms from query logs. In this paper we present and discuss results on mining large query log induced graphs, and how they contribute to query classification and to understand user intent and interest. Our approach consists on effi-ciently obtaining a hierarchical clustering for such graphs and, then, a hierarchical query folksonomy. Results obtained with real data provide interesting insights on semantic relations among queries and are com-pared with conventional taxonomies, namely the ODP categorization. 1