Fuzzy ontologies building method: Fuzzy OntoMethodology

Ghorbel, Hanen; Bahri, Afef; Bouaziz, Rafik

doi:10.1109/nafips.2010.5548211

Cited by 16 publications

(14 citation statements)

References 11 publications

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“…• Zadeh's fuzzy set theory (Lam, 2006;Sanchez, 2006;Quan et al, 2006b;Yeung & Leung, 2006;Calegari & Ciucci, 2007;Ghorbel et al, 2010;Cai & Leung, 2011;Elleuch et al, 2011;Singh et al, 2011); • intuitionistic fuzzy set (Zhai et al, 2007(Zhai et al, , 2008; • T2FS (Lee et al, 2010); • compensatory fuzzy logic (Valdés et al, 2011); • fuzzy rough set (Klinov & Mazlack, 2006;; • dynamic fuzzy logic (Calegari & Loregian, 2006;Cui et al, 2009). …”

Section: Categories Of Fuzzy Ontologiesmentioning

confidence: 99%

“…Abulaish and Dey (2007) developed a FOGA for handling uncertainties and non-uniformity in domain knowledge description. Also, Ghorbel et al (2010) defined a new fuzzy ontology building methodology called Fuzzy OntoMethodolog, which consists of three-step conceptualization, ontologization, and operationalization. Also, Ghorbel et al (2010) defined a new fuzzy ontology building methodology called Fuzzy OntoMethodolog, which consists of three-step conceptualization, ontologization, and operationalization.…”

Section: Construction Of Fuzzy Ontologiesmentioning

confidence: 99%

See 1 more Smart Citation

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

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Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains, in this paper, we provide a comprehensive overview of fuzzy ontologies. In detail, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.ontology definition languages, such as RDF(S), SHOE, OIL, DAML, DAML + OIL, and web ontology language (OWL), have been developed over the past years (Horrocks et al., 2003). The OWL (Smith et al., 2004) and its successor OWL 2 (Cuenca Grau et al., 2008) is the W3C recommended ontology representation language. The theoretical underpinnings of OWL and ontologies are strongly based on Description Logics (DLs), a subset of first-order logic especially suitable for representing structured knowledge (Horrocks & Sattler, 2001;Baader et al., 2003;Horrocks et al., 2003).Although ontology is a quite expressive formalism, it features limitations, mainly with what can be said about fuzzy information. Nowadays, in ontology-based and many applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications of the Semantic Web, such as knowledge management, e-commerce, and web portals. For example, a task like a 'doctor appointment' could look like: 'Make me an appointment with a doctor close to my home not too early and of good references' (Stoilos et al., 2010). The conceptual formalism supported by typical ontology may not be sufficient to represent such information and knowledge. Therefore, many proposals have attempted to apply different formalisms...

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Section: Categories Of Fuzzy Ontologiesmentioning

confidence: 99%

Section: Construction Of Fuzzy Ontologiesmentioning

confidence: 99%

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…There are many fuzzy ontology construction methodologies as IKARUS-Onto [40], UFOC [65], UPFON [66] and OntoMethodology [67]. Moreover, fuzzy ontology representation languages have been proposed in [63,68].…”

Section: 321mentioning

confidence: 99%

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

El–Sappagh

Elmogy

Riad

2015

Artificial Intelligence in Medicine

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“…In a more recent research on building fuzzy ontologies [9] the definition of a fuzzy ontology separates out the crisp and the fuzzy components, that is, crisp concepts, properties and relations from the fuzzy concepts, properties, and relations. A fuzzy ontology is defined as a 7-tuple OF = <C, P, C F , P F , R, R F , As F , A F , A>.…”

Section: A Definitions Of Fuzzy Ontologiesmentioning

confidence: 99%

Fuzzy ontologies: The state of the art

Cross

2014

2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW)

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Ontologies play a central role in the SemanticWeb. Much research has focused on extending ontologies and Semantic Web technologies to handle uncertain information. This paper provides a review of recent approaches for extending ontologies to fuzzy ontologies. Some approaches have been developed for application specific purposes, for example for information retrieval and search on the Semantic Web and are not formally relying on current Semantic Web technologies such as standard web ontology languages and tools such as ontology editors or reasoners. Other approaches emphasize developing methods to extend Semantic Web standard technologies with fuzzy logic and fuzzy reasoning.

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Fuzzy ontologies building method: Fuzzy OntoMethodology

Cited by 16 publications

References 11 publications

A survey on fuzzy ontologies for the Semantic Web

A survey on fuzzy ontologies for the Semantic Web

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

Fuzzy ontologies: The state of the art

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