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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.

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Data integration becomes even more necessary given the increasing availability of data from distributed and heterogeneous sources. To address such heterogeneity, crisp ontologies have been employed in order to represent the semantics of integrated data. However, it is interesting to use fuzzy logic concepts in these ontologies for a more expressive representation of vague information relevant to some domains. In this context, this paper presents DISFOQuE system for data integration based on fuzzy ontology, which provides a homogeneous view of data sources and also performs query expansions in order to retrieve more comprehensive answers for the user. We have executed a real experiment in the domain of watershed analysis, which provided a homogeneous view of the watershed data sources and more e®ective answers to researchers.

Ontologies have been employed in applications that require semantic information representation and processing. However, traditional ontologies are not 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.