Artificial Intelligence in Medicine
| Hi-index | 0.00 |
Ontologies have been successfully employed in applications that require semantic information processing. However, traditional ontologies are less suitable to express fuzzy or vague information, which often occurs in human vocabulary as well as in several application domains. In order to deal with such restriction, concepts from fuzzy set theory should be incorporated into ontologies so that it is possible to represent and reason over fuzzy or vague knowledge. In this context, this paper proposes a meta-ontology approach for representing fuzzy ontologies covering fuzzy properties, fuzzy rules, and fuzzy reasoning methods such as classical and general fuzzy reasoning, aiming to support the classification of new individuals based on rules containing fuzzy properties.