Fuzzy ontology implementation and query answering on databases

Bahri, Afef; Bouaziz, Rafik; Gargouri, Faı̈ez

doi:10.1109/nafips.2009.5156413

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…Bahri et al (2009) addressed the problem of implementation and query answering of fuzzy ontologies on databases. Bahri et al (2009) addressed the problem of implementation and query answering of fuzzy ontologies on databases.…”

Section: Query and Storage Of Fuzzy Ontologiesmentioning

confidence: 99%

“…Barranco et al . (2007) presented a schema structure to store ontologies and their instances in a fuzzy object relation database management system capable of handling fuzzy data types. The proposed schema covers the main constructs of OWL ontologies and sets the groundwork necessary to develop internal reasoning in the database.…”

Section: Applications and Other Issues Of Fuzzy Ontologiesmentioning

confidence: 99%

“…Constructing fuzzy ontologies based on formal concept analysis theory (Quan et al, 2006a(Quan et al, , 2006bChen et al, 2009;De Maio et al, 2009;Cross & Kandasamy, 2011) Constructing fuzzy ontologies from fuzzy database models (Blanco et al, 2005(Blanco et al, , 2008Ma et al, , 2010Ma et al, , 2011aMa et al, , 2011bZhang et al, 2008aZhang et al, , 2008bZhang et al, , 2011bZhang et al, , 2013aZhang et al, , 2013bZhang et al, , 2015) Constructing fuzzy ontologies from other data sources such as fuzzy narrower terms, fuzzy relations, among others (Widyantoro & Yen, 2001a, 2001bNikravesh et al, 2004;Angryk et al, 2006;Ceravolo et al, 2006;Nováček & Smrž, 2006;Ling et al, 2007;Tafazzoli & Sadjadi, 2008;Ghorbel et al, 2010;Inyaem et al, 2010;Alexopoulos et al, 2012) Querying over lightweight fuzzy DL ontologies (Straccia, 2006c;Pan et al, 2007Pan et al, , 2008 Querying over expressive fuzzy DL ontologies (Mailis et al, 2007;Cheng et al, 2008bCheng et al, , 2009aCheng et al, , 2009b Querying over fuzzy ontologies based on fuzzy relational databases (Buche et al, 2005;Bahri et al, 2009) Other fuzzy ontology query approaches (Widyantoro & Yen, 2001a…”

Section: Construction Query and Storagementioning

confidence: 99%

See 2 more Smart Citations

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: Query and Storage Of Fuzzy Ontologiesmentioning

confidence: 99%

Section: Applications and Other Issues Of Fuzzy Ontologiesmentioning

confidence: 99%

Section: Construction Query and Storagementioning

confidence: 99%

See 1 more Smart Citation

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

“…Bahri et al implemented fuzzy ontology for query answering on databases. They proposed a language to define fuzzy ontology schema and to query fuzzy ontology databases and an inferential engine to infer fuzzy concept instances and their membership degrees [29].…”

Section: Related Workmentioning

confidence: 99%

“…Bahri et al addressed fuzzy ontology implementation and query answering on databases. They propose a language to define fuzzy ontology schema and to query fuzzy ontology databases and an inferential engine to infer fuzzy concept instances and their membership degrees [15]. Pan et al proposed a framework of fuzzy query languages for fuzzy ontologies and present ed query answering algorithms for query languages over fuzzy DL-Lite ontologies [16].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Information Retrieval Approach Based on Two Degrees of Uncertainty Fuzzy Ontology

Hourali

Montazer

2011

Advances in Fuzzy Systems

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In spite of the voluminous studies in the field of intelligent retrieval systems, effective retrieving of information has been remained an important unsolved problem. Implementations of different conceptual knowledge in the information retrieval process such as ontology have been considered as a solution to enhance the quality of results. Furthermore, the conceptual formalism supported by typical ontology may not be sufficient to represent uncertainty information due to the lack of clear-cut boundaries between concepts of the domains. To tackle this type of problems, one possible solution is to insert fuzzy logic into ontology construction process. In this article, a novel approach for fuzzy ontology generation with two uncertainty degrees is proposed. Hence, by implementing linguistic variables, uncertainty level in domain's concepts (Software Maintenance Engineering (SME) domain) has been modeled, and ontology relations have been modeled by fuzzy theory consequently. Then, we combined these uncertain models and proposed a new ontology with two degrees of uncertainty both in concept expression and relation expression. The generated fuzzy ontology was implemented for expansion of initial user's queries in SME domain. Experimental results showed that the proposed model has better overall retrieval performance comparing to keyword-based or crisp ontology-based retrieval systems.

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Querying Fuzzy Description Logics and Ontology Knowledge Bases

Zhang

et al. 2013

Fuzzy Knowledge Management for the Semantic Web

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Fuzzy ontology implementation and query answering on databases

Cited by 9 publications

References 2 publications

A survey on fuzzy ontologies for the Semantic Web

A survey on fuzzy ontologies for the Semantic Web

An Intelligent Information Retrieval Approach Based on Two Degrees of Uncertainty Fuzzy Ontology

Querying Fuzzy Description Logics and Ontology Knowledge Bases

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