A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference

Abstract

Fuzzy ontologies have been employed to represent and reason over fuzzy information, which often occurs in real-world applications. Fuzzy inference systems (FIS) are well-known computational intelligence systems whose inferences can also be exploited in fuzzy ontology-based applications. Specifically, the combination of fuzzy ontologies and Mamdani-type FIS can provide inferences involving fuzzy rules and numerical property values, which can be considered in other fuzzy ontology reasoning tasks. In this sense, this paper proposes a hybrid reasoner combining fuzzy ontology and Mamdani inference to provide meaningful inferences that are not available to fuzzy ontologybased applications in an integrated way. Fuzzy rules are represented with Fuzzy Markup Language, providing an abstraction level with regard to the underlying FIS implementation. Some experiments are presented regarding a recommender system context, including a comparison with a fuzzy description logic reasoner in terms of fuzzy rule reasoning semantics and integration issues.