document: Unsupervised query categorization using automatically-built concept graphs 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 .
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