Fuzzy MCGDM Approach for Ontology Fuzzification

Abstract

This paper extends a novel method for fuzzifying crisp ontologies through a fuzzy Multi-Criteria Group Decision-Making (MCGDM) approach. The key feature of the method is the achievement of a geometric compromise obtained by minimising distances among the best alternatives provided by experts, and by assigning and refining membership degrees for entities and relations in order to better capture uncertainty and vagueness. Its effectiveness is demonstrated on two case studies, the Cognitive Task Ontology (CogiTO) and the BrainTeaser Ontology (BTO), which showcase the potential of the proposed method in complex decision-making scenarios. Many applications are possible, including the enhancement of knowledge integration and the development of more informative reasoning under uncertainty.