A fuzzy ontology-based semantic data integration system

Yaguinuma, Cristiane A.; Afonso, Gustavo F.; Ferraz, Vinícius Ramos Toledo; Borges, Sérgio R.; Santos, Marilde Terezinha Prado

doi:10.1109/iri.2010.5558938

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Much work has been carried out toward extending ontologies with different logical formalisms to meet the application requirements. In general, several extension formalisms of ontologies can be distinguished, including the extensions of ontologies based on : Zadeh’s fuzzy set theory (Lam, 2006; Sanchez, 2006; Quan et al ., 2006b; Yeung & Leung, 2006; Abulaish & Dey, 2007; 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, 2008); T2FS (Lee et al ., 2010); compensatory fuzzy logic (Valdés et al ., 2011); fuzzy rough set (Klinov & Mazlack, 2006; Dey et al ., 2007); dynamic fuzzy logic (Calegari & Loregian, 2006; Cui et al ., 2009). …”

Section: Representation Of Fuzzy Ontologiesmentioning

confidence: 99%

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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: Representation Of Fuzzy Ontologiesmentioning

confidence: 99%

“…Zadeh’s fuzzy set theory (Lam, 2006; Sanchez, 2006; Quan et al ., 2006b; Yeung & Leung, 2006; Abulaish & Dey, 2007; Calegari & Ciucci, 2007; Ghorbel et al ., 2010; Cai & Leung, 2011; Elleuch et al ., 2011; Singh et al ., 2011);…”

Section: Representation 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

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“…LAU R. et al [18] illustrated a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology automatic extraction algorithm. C. A. Yaguinuma et al [19] tried to use fuzzy logic concepts in crisp ontologies for a more expressive representation of vague information relevant to some domains, and presented DISFOQuE system for data integration based on fuzzy ontology.…”

Section: A Ontology and Fuzzy Ontologymentioning

confidence: 99%

Scientific Ontology Construction Based on Interval Valued Fuzzy Theory Under Web 2.0

Xue¹,

Jia²,

Jiang³

et al. 2013

JSW

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Construction of the unified and shared domain ontology is significant for effective knowledge management. For the acquisition and sharing of scientific research knowledge under Web2.0, a novel approach of building Interval Valued Fuzzy Ontology (IVFO) in scientific research domain is presented. Through interval valued fuzzy theory, the definition and constructing framework of IVFO is proposed. Then IVFO is applied to semi-automatic extraction of information retrieval research domain. The preliminary constructing of research domain ontology is an essential base for the knowledge management system of scientific research. It can be effective methods for enhancing the efficiency and productivity of researching.

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“…Another body of active relevant research is devoted to theories of fuzzy sets, probabilistic databases, and imprecision in data management [10,11]. It addresses uncertain databases and explanatory databases in which possible data worlds exist with certain probabilities and that require vague/imprecise information to describe them.…”

Section: Related Workmentioning

confidence: 99%

“…By enforcing fuzzy constraints on data and related metadata, the user query is reformulated to return approximate results. This leads to another relevant approach often referred as federation that advocates query reformulation and expansion to provide a homogeneous view across several data sources [10,7]. This, however, requires query reformulation and adjustment whenever a new data source comes in.…”

Section: Related Workmentioning

confidence: 99%

Learning Unified Famous Objects (UFO) to bootstrap information integration

Gubanov

Shapiro

Pyayt

2011

2011 IEEE International Conference on Information Reuse &Amp; Integration

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For at least a decade, WWW, large enterprises, and desktop users suffer from an inability to efficiently access and manage their data. To help automate different aspects of this challenging problem many solutions have been proposed in both academic and industrial research. One of them -UFO Repository, introduced in [5] is currently gaining momentum by advocating an object-oriented approach to help manage information overflow.An overview and evaluation of the UFO approach was published and reviewed in [5,1]. This paper is more focused on the algorithms for UFO creation and knowledge accumulation. We describe, evaluate these algorithms, and demonstrate that UFO learning performance is surprisingly fast and accurate across several domains even for a small amount of initial training data.

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A fuzzy ontology-based semantic data integration system

Cited by 9 publications

References 13 publications

A survey on fuzzy ontologies for the Semantic Web

A survey on fuzzy ontologies for the Semantic Web

Scientific Ontology Construction Based on Interval Valued Fuzzy Theory Under Web 2.0

Learning Unified Famous Objects (UFO) to bootstrap information integration

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