01912naa a2200253 a 450000100080000000500110000800800410001902400510006010000210011124501180013226000090025030000080025949000690026752009910033665300190132765300200134665300430136665300350140970000340144470000210147870000190149970000170151877301230153520003322024-02-06       2014    bl uuuu     u00u1 u    #d7 ahttps://link.springer.com/bookseries/79112DOI1 aYAGUINUMA, C. A.  aCombining fuzzy ontology reasoning and mamdani fuzzy inference system with HyFOM reasoner.h[electronic resource]  c2014  a190  a(Lecture Notes in Business Information Processing, v. 190.)v190  aRepresenting and processing imprecise knowledge has been a requirement for a number of applications. Some real-world domains as well as human subjective perceptions are intrinsically fuzzy, therefore conventional formalisms may not be sufficient to capture the intended semantics. In this sense, fuzzy ontologies and Mamdani fuzzy inference systems have been successfully applied for knowledge representation and reasoning. Combining their reasoning approaches can lead to inferences involving fuzzy rules and numerical properties from ontologies, which can be required to perform other fuzzy ontology reasoning tasks such as the fuzzy instance check. To address this issue, this paper describes the HyFOM reasoner, which follows a hybrid architecture to combine fuzzy ontology reasoning with Mamdani fuzzy inference system. A real-world case study involving the domain of food safety is presented, including comparative results with a state-of-the-art fuzzy description logic reasoner.  aFuzzy ontology  aHybrid reasoner  aKnowledge representation and reasoning  aMamdani fuzzy inference system1 aMAGALHAES JUNIOR, W. C. P. de1 aSANTOS, M. T. P.1 aCAMARGO, H. A.1 aREFORMAT, M.  tIn: HAMMOUDI, S., CORDEIRO, J., MACIASZEK, L., FILIPE, J. (ed.). Enterprise information systems. Cham: Springer, 2014.