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Personalizing information retrieval in CRISs with Fuzzy Sets and Rough Sets

Current Research Information Systems (CRISs) usually contain large amounts of heterogeneous and distributed data, which makes finding specific information difficult for a user. It is in these cases that the concept of a personal search agent, proactively informing the user about newly available information, becomes more and more popular. But how can the agent know what is useful for the user if he has not expressed it explicitly? Our approach proposes using fuzzy and rough sets to make the matching process between the users' interests and the information in the system more flexible, as they allow expressing partial relationships and expanding queries, as well as dealing with problems like imprecision, ambiguity, or incompleteness.