Image indexing and retrieval using expressive fuzzy description logics

Simou, Nikolaos; Athanasiadis, Thanos; Stoilos, Giorgos; Kollias, Stefanos

doi:10.1007/s11760-008-0084-1

Cited by 35 publications

(27 citation statements)

References 22 publications

(27 reference statements)

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“…Extraction and analysis of image semantics (Papadopoulos et al, 2006;Simou et al, 2008;Dasiopoulou et al, 2009Dasiopoulou et al, , 2010) Semantic content extraction in videos (Yildirim et al, 2013) The fuzzy ontologies are suitable for expressing semantics in a formal machine-processable representation that will allow automatic analysis and further processing of the extracted semantic descriptions Knowledge mining, clustering, and integration…”

Section: Semantics Extraction and Analysismentioning

confidence: 99%

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: Semantics Extraction and Analysismentioning

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

“…However, in the Semantic Web, there is much imprecise and uncertain knowledge, which cannot be represented by these rule languages. To this end, based on OWL DL, that is, the DL SHOIN(D), Pan et al (2006a) Simou et al (2008aSimou et al ( , 2008b, Straccia and Visco (2007), Straccia (2010), Singh et al (2004) and Stoilos et al (2005d) Sanchez andYamanoi (2006), Stoilos et al (2005cStoilos et al ( , 2010Stoilos et al ( , 2006c, Gao and Liu (2005), Calegari and Ciucci (2007) and Bobillo and Straccia (2009c) f-SWRL: Pan et al Straccia (2006cStraccia ( , 2004b, Venetis et al (2007) and Zhao and Boley (2008) Lukasiewicz (2006) and Straccia (2007a, 2007b) Medicine: Molitor and Tresp (2000), D'Aquin et al (2006) and Schlobach et al (2007) Ontology mpping : Nova´cˇek and Smrzˇ(2006), Ferrara et al (2008) and Xu et al (2005) Electronic market: Ragone et al (2008aRagone et al ( , 2008bRagone et al ( , 2008cRagone et al ( , 2007 and Agarwal and Lamparter (2005) Data modeling: Zhang et al (2008Zhang et al ( , 2009) Semantic Web portals: Zhang et al (2005) Semantic search engines: Li et al (2007Li et al ( , 2006d Image analysis: …”

Section: Fuzzy Extensions Of the Semantic Web Languagesmentioning

confidence: 99%

“…Simou et al (2008a) proposed a methodology for semantic indexing and retrieval of images based on the fuzzy DL f-SHIN. The idea has also been adopted by Stoilos et al (2005d), Singh et al (2004), and Simou et al (2008b) for information representation and retrieval.…”

Section: Some Particular Applications Of Fuzzy Description Logicsmentioning

confidence: 99%

An overview of fuzzy Description Logics for the Semantic Web

Zhang

Wang

et al. 2012

The Knowledge Engineering Review

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Information imprecision and uncertainty exist in many real world applications, and such information would be retrieved, processed, shared, reused, and aligned in the maximum automatic way possible. As a popular family of formally well-founded and decidable knowledge representation languages, fuzzy Description Logics (fuzzy DLs), which extend DLs with fuzzy logic, are very well suited to cover for representing and reasoning with imprecision and uncertainty. Thus, a requirement naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web, because DLs are the logical foundation of the Semantic Web. Currently, there have been lots of fuzzy extensions of DLs with Zadeh's fuzzy logic theory papers published, to investigate fuzzy DLs and more importantly serve as identifying the direction of fuzzy DLs study. In this paper, we aim at providing a comprehensive literature overview of fuzzy DLs, and we focus our attention on fuzzy extensions of DLs based on fuzzy set theory. Other relevant formalisms that are based on approaches like probabilistic theory or non-monotonic logics are covered elsewhere. In detail, we first introduce the existing fuzzy DLs (including the syntax, semantics, knowledge base, and reasoning algorithm) from the origin, development (from weaker to stronger in expressive power), some special techniques, and so on. Then, the other important issues on fuzzy DLs, such as reasoning, querying, applications, and directions for future research, are also discussed in detail. Also, we make a comparison and analysis.

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“…In [46], fuzzy DLs reasoning is proposed to support the refinement of an initial set of over-segmented image regions and their classifications, in terms of region merging and update of classification degrees based on those of its neighboring regions. In [16], a fuzzy DLs based reasoning framework is proposed to integrate, possibly complementary, overlapping or conflicting classifications at object and scene level, into a semantically coherent final interpretation.…”

Section: Representing and Handling Imprecisionmentioning

confidence: 99%

Trends and Issues in Description Logics Frameworks for Image Interpretation

Dasiopoulou

Kompatsiaris

2010

Artificial Intelligence: Theories, Models and Applications

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Abstract. Description Logics have recently attracted significant interest as the underlying formalism for conceptual modelling in the context of high-level image interpretation. Differences in the formulation of image interpretation semantics have resulted in varying configurations with respect to the adopted modelling paradigm, the utilised form of reasoning, and the way imprecision is managed. In this paper, we examine the relevant literature, outlining the corresponding strengths and weaknesses, and argue that although coming up with a complete solution is hard to envisage any time soon, there are a number of key considerations that may serve as guidelines towards this direction.

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Image indexing and retrieval using expressive fuzzy description logics

Cited by 35 publications

References 22 publications

A survey on fuzzy ontologies for the Semantic Web

A survey on fuzzy ontologies for the Semantic Web

An overview of fuzzy Description Logics for the Semantic Web

Trends and Issues in Description Logics Frameworks for Image Interpretation

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