A Meta-Ontology for Modeling Fuzzy Ontologies and its Use in Classification Tasks based on Fuzzy Rules

Abstract

Ontologies have been employed in applications that require semantic information representation and processing. However, traditional ontologies are not particularly suitable to express fuzzy or vague information, which often occurs in human vocabulary as well as in several application domains. To deal with this limitation, concepts from the Fuzzy Set Theory can be incorporated into ontologies making it possible to represent and reason over fuzzy or vague knowledge. In this context, this paper proposes Fuzz-Onto, a meta-ontology for representing fuzzy ontologies which, so far, models fuzzy concepts, fuzzy relationships and fuzzy properties. In particular, the representation of fuzzy properties and linguistic terms makes it possible to combine fuzzy modeling in ontologies with existing fuzzy rule-based classification methods. The paper also presents a case study in the knowledge domain of scientific documents as an instantiation of the modeling-inference articulation.