Unsupervised Query Categorization using Automatically-Built Concept Graphs

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Abstract

Automatic categorization of user queries is an important component of general purpose (Web) search engines, particularly for triggering rich, query-specific content and sponsored links. We propose an unsupervised learning scheme that reduces dramatically the cost of setting up and maintaining such a categorizer, while retaining good categorization power. The model is stored as a graph of concepts where graph edges represent the cross-reference between the concepts. Concepts and relations are extracted from query logs by an offline Web mining process, which uses a search engine as a powerful summarizer for building a concept graph. Empirical evaluation indicates that the system compares favorably on publicly available data sets (such as KDD Cup 2005) as well as on portions of the current query stream of Yahoo! Search, where it is already changing the experience of millions of Web search users.