A Fuzzy Ontology Generation Framework and Its Application to Subjective Credit Reporting Management
In this paper, we construct a fuzzy ontology model and propose using a fuzzy matching rule base to promote a fuzzy ontology generation frame which supports rough concept descriptions on intrinsic semantic level. To consider the rule base and express the fuzziness, the formal analysis of concept vector is developed for the generation of fuzzy ontology that can deal with uncertain information. The proposed fuzzy semantic extension technique taking advantage of the fuzzy matching rules consists of the following steps: vectorization of fuzzy concept, establishment of basic concept pair, and synthesis of semantic information. The formal descriptions of fuzzy approximations of concepts and valid fuzzy semantic reasoning can be obtained through semantic matching of concepts. As such, applying the provided frame to subjective credit reporting management, the SCRM ontology model will potentially improve the fuzzy concepts reasoning and effectively facilitate the semantic expansion. It is, especially, suitable for acquiring acceptable degree of subjects through the intelligent reasoning of trust relationship.