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An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. The authors study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. They use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features.
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