An efficient rule acquisition method using type - 2 fuzzy rough ontology

Rule acquisition acts as a heart of the Semantic Web applications. It is well known that the Semantic Web which implies inferential rules are inconsistent. To tackle this problem, this paper presents a new rule acquisition method based on type - 2 fuzzy rough ontology and self adaptive genetic algorithm to deal with inconsistent data on Web. In this research, we use type-2 fuzzy rough ontology to provide efficient inference mechanisms and reasoning capabilities, which deduce inconsistent and ambiguous data on the Web. Then self adaptive genetic algorithm is used for mining effective rules set. When the algorithm is processing, the user is allowed to set three evaluation parameter values of the rules: support confidence and coverage for specific application needs. The proposed algorithm can remove the rules which do not meet the requirements. The case study results show that the suggested method is suitable to provide efficient and consistent rules on Web.