A Smart Query Formulation for an Efficient Web Search

Traditional search engines rely on keyword-based matching, recovering the documents which present some occurrences of the input keywords, but ignore at all the data meaning of the retrieved documents. Thus, long lists of pages links are returned but actually only a handful of pages contain reference to relevant web resources and meet the needs of users. The exigency of major awareness in the interpretation of web data yields new approaches and methodologies for improving the web search and retrieval, by taking into account the context of information, related to the user query. This work presents an approach for supporting the user in the Web search activity: it achieves the interpretation of the input query and, on the basis of the the local knowledge, replies by providing (links of) web pages which are more relevant to the content meaning of the input query. The approach combines intrinsic potential of the agent-based paradigm with the modeling of knowledge through techniques of soft computing. The agents encode the semantics of data, by exploiting ontologies, in order to grasp the actual query meaning. The information elicited by the query interpretation represents an add-on, aimed at augmenting the system knowledge, exploited in the discovery of web pages which match the user request. c ∞ 2007

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