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User logs of a popular search engine keep track of user activities including user queries, user click-through from the returned list, and user browsing behaviors. Knowledge about user queries discovered from user logs can improve the performance of the search engine. We propose a data-mining approach that produces generalized query patterns or templates from the raw user logs of a popular commercial knowledge-based search engine that is currently in use. Our simulation shows that such templates can improve search engine's speed and precision, and can cover queries not asked previously. The templates are also comprehensible so web editors can easily discover topics in which most users are interested.