Ontology Aggregation with Maximum Consensus Based on a Fuzzy Multi-criteria Group Decision-Making Method

In this work, we extend the integration of fuzzy logic in Multi-Criteria Group Decision-Making (MCGDM) problems and its application to ontologies. We define an MCGDM framework where experts assign scores and weights to ontology classes, and each one of them is assigned a fuzzy weight, reflecting their relative importance in the decision process. Each expert selects their best choice among the alternatives and a final best compromise A∗ is derived using a minimal mean distance operator, ensuring that the aggregated result optimally reflects expert opinions while minimizing deviations from individual preferences.