Main Article Content
As the number of available Web pages grows, users experience increasing difficulty finding documents relevant to their interests. One of the underlying reasons for this is that most search engines find matches based on keywords, regardless of their meanings. To provide the user with more useful information, we need a system that includes information about the conceptual frame of the queries as well as its keywords. This is the goal of KeyConcept, a search engine that retrieves documents based on a combination of keyword and conceptual matching. Documents are automatically classified to determine the concepts to which they belong.
Query concepts are determined automatically from a small description of the query or explicitly entered by the user. This paper describes the system architecture, the training of the classifier, and the results of our experiments evaluating system performance. KeyConcept is shown to significantly improve search result precision through its use of conceptual retrieval.
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