Mining Fuzzy Ontology for a Web-Based Granular Information Retrieval System

Lau, Raymond Y. K.; Lai, Chapmann C.L.; Li, Yuefeng

doi:10.1007/978-3-642-02962-2_30

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…Calegari and Sanchez (2008) showed how a fuzzy ontology-based approach can improve semantic documents retrieval. Also, Lau et al (2009) illustrated the design and development of a fuzzy ontology-based granular information retrieval system to facilitate domain-specific search. Also, Lau et al (2009) illustrated the design and development of a fuzzy ontology-based granular information retrieval system to facilitate domain-specific search.…”

Section: Information Retrievalmentioning

confidence: 99%

“…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: Information Retrievalmentioning

confidence: 99%

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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“…Ontology-based semantic retrieval is very useful for specific-domain environments. A general IR system to facilitate specific domain search is illustrated in [42]. The system uses fuzzy ontologies and is based on the notion of information granulation, a novel computational model is developed to estimate the granularity of documents.…”

Section: Ontologies In Retrieval Systemsmentioning

confidence: 99%

A conceptual representation of documents and queries for information retrieval systems by using light ontologies

Dragoni

Pereira

Tettamanzi

2012

Expert Systems with Applications

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This article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach.

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“…Accordingly, there is a pressing need for an alternative computational model capable of facilitating domain-specific IR. Granularity-based IR (or granular IR) aims to find documents that are not only similar to a query but which also satisfy a specific granularity requirement defined in relation to a wide granularity spectrum of semantically general and semantically specific information [Lau et al 2009b]. Because generality is the antonym of specificity, we can estimate the granularity (i.e., an attribute or property) of a document in terms of its informational generality (i.e., an attribute value).…”

Section: The Needs For Granularity-based Irmentioning

confidence: 99%

Toward a semantic granularity model for domain-specific information retrieval

Yan

Lau

Song

et al. 2011

ACM Trans. Inf. Syst.

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Both similarity-based and popularity-based document ranking functions have been successfully applied to information retrieval (IR) in general. However, the dimension of semantic granularity also should be considered for effective retrieval. In this paper, we propose a semantic granularity based IR model which takes into account the three dimensions, namely similarity, popularity, and semantic granularity, to improve domain-specific search. In particular, a concept-based computational model is developed to estimate the semantic granularity of documents with reference to a domain ontology. Semantic granularity refers to the levels of semantic detail carried by an information item. The results of our benchmark experiments confirm that the proposed semantic granularity based IR model performs significantly better than the similarity-based baseline in both a bio-medical and an agricultural domain. In addition, a series of user-oriented studies reveal that the proposed document ranking functions resemble the implicit ranking functions exercised by humans. The perceived relevance of the documents delivered by the granularity-based IR system is significantly higher than that produced by a popular search engine for a number of domain-specific search tasks. To the best of our knowledge, this is the first study regarding the application of semantic granularity to enhance domain-specific IR.

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Mining Fuzzy Ontology for a Web-Based Granular Information Retrieval System

Cited by 7 publications

References 7 publications

A survey on fuzzy ontologies for the Semantic Web

A survey on fuzzy ontologies for the Semantic Web

A conceptual representation of documents and queries for information retrieval systems by using light ontologies

Toward a semantic granularity model for domain-specific information retrieval

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