A Fuzzy Ontology for Database Querying with Bipolar Preferences

The expression and the evaluation of complex user preferences in the context of distributed and heterogeneous information systems are tackled in this paper. Complex preferences are modeled by fuzzy bipolar conditions, which associate negative and positive conditions. Queries involving such conditions are called bipolar queries. In our case, such queries are addressed to information systems such as Web applications, built on several distributed and heterogeneous databases. This querying can lead to process huge volumes of data and can deliver massive responses, in which it is difficult to the user to distinguish the relevant answers from irrelevant ones. Semantic aspects make it possible to address this problem by providing a personalized data access method, so that only the most relevant data are targeted to evaluate queries. We introduce then in this paper a new approach for flexible querying of complex information systems that combines a reasoning mechanism (an ontology‐based on the fuzzy bipolar DLR‐Lite) with a bipolar relational language of a high expressivity (Bipolar SQLf language). The reasoning mechanism can also answer queries in approximative way, based on degrees expressing at which extent it is possible to substitute a concept in the query with other concepts, while still meaningful to the user.

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