Natural language in general, and web-data specifically, are pervaded with concepts that are vague, specifically fuzzy, in the sense that statements, such as “the indicator of a country’s standard of living is low”, cannot always to be determined to be either true or false because it is unclear how to define exactly the involved term “low GDP”. However, the restriction of SWLs to classical, two-valued/bivalent logic has limitations related to its inability to semantically cope with the inherent “imperfection” of web-data. In a fuzzy setting the notion of “relevance” or “aboutness” is indeed context dependent and subjective. Concerning point 1, both the determination of the relevant source involves fuzziness as well the representation of the score of the degree of relevance of each source associated to a query. In Ontology-based Matchmaking typically a buyer specifies his graded preferences over the product he wants to buy, while on the other hand sellers specifies theirs.