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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 templatesdoi:10.1109/hicss.2001.926534 dblp:conf/hicss/LingGZQZ01 fatcat:suvir6p7tfgvphe4y7icmx5vam