Abstract: The continuing growth of the Internet contents makes difficult the information access, inducing the task of information retrieval highly critical. The search engines often return a huge quantity of Web data, which is irrelevant to the input query. The emergency of personalization in the web search activities demands stable synergies for retrieving relevant information which meets the user needs. This work proposes an agent-based system for supporting customized Web searches. The system replies to a typical web query providing ad-hoc user-profiled links to web pages. the basis of a learning activity, which constitute an initial knowledge The agents collect locally the knowledge during an initial user querying/answering interaction phase and then interpret the meaning of collected information, by exploiting ontologies: they discover new semantic correlations among query terms, in order to refine the description of queries. These queries are used in the web search to provide more relevant replies, which reflect the user preferences and interest. This proposal represents a valid support for evidence-based applications, where sensitive contexts such as health care, medicine require high quality and unambiguous information in the specialized lexical domain.
Keywords: Personalized web search, decision making, multi-agent systems, clustering, ontologies