Authors:
Cristiane A. Yaguinuma
1
;
Walter C. P. Magalhães Jr.
2
;
Marilde T. P. Santos
1
;
Heloisa A. Camargo
1
and
Marek Reformat
3
Affiliations:
1
Federal University of São Carlos, Brazil
;
2
Embrapa Dairy Cattle, Brazil
;
3
University of Alberta, Canada
Keyword(s):
Knowledge Representation and Reasoning, Fuzzy Ontology, Mamdani Fuzzy Inference System, Hybrid Reasoner.
Related
Ontology
Subjects/Areas/Topics:
Advanced Applications of Fuzzy Logic
;
Applications of Expert Systems
;
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
;
Strategic Decision Support Systems
Abstract:
Some real-world applications require representation and reasoning regarding imprecise or vague information. In this context, the appropriate combination of fuzzy ontologies and Mamdani fuzzy inference systems can provide meaningful inferences involving fuzzy rules and numerical property values. In general, this knowledge is not obtained through typical fuzzy ontology reasoning and can be relevant for some ontology reasoning tasks that depend on numerical property values. To address this issue, this paper proposes the HyFOM reasoner, which provides a hybrid integration of fuzzy ontology and Mamdani reasoning. 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.