Ontology Instance Matching based MPEG-7 Resource Integration

Ontology Instance Matching based MPEG-7 Resource Integration

Hanif Seddiqui, Masaki Aono
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 16
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781609604509|DOI: 10.4018/jmdem.2010040102
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MLA

Seddiqui, Hanif, and Masaki Aono. "Ontology Instance Matching based MPEG-7 Resource Integration." IJMDEM vol.1, no.2 2010: pp.18-33. http://doi.org/10.4018/jmdem.2010040102

APA

Seddiqui, H. & Aono, M. (2010). Ontology Instance Matching based MPEG-7 Resource Integration. International Journal of Multimedia Data Engineering and Management (IJMDEM), 1(2), 18-33. http://doi.org/10.4018/jmdem.2010040102

Chicago

Seddiqui, Hanif, and Masaki Aono. "Ontology Instance Matching based MPEG-7 Resource Integration," International Journal of Multimedia Data Engineering and Management (IJMDEM) 1, no.2: 18-33. http://doi.org/10.4018/jmdem.2010040102

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Abstract

Heterogeneous multimedia contents are annotated by a sharable formal conceptualization, often called ontology, and these contents, regardless of their media, become sharable resources/instances. Integration of the sharable resources and acquisition of diverse knowledge is getting researchers’ attention at a rapid pace. In this regard, MPEG-7 standard convertible to semantic Resource Description Framework (RDF) evolves for containing structured data and knowledge sources. In this paper, the authors propose an efficient approach to integrate the multimedia resources annotated by the standard of MPEG-7 schema using ontology instance matching techniques. MPEG-7 resources are usually specified explicitly by their surrounding MPEG-7 schema entities, e.g., concepts and properties, in conjunction with other linked resources. Therefore, resource integration needed schema matching as well. In this approach, the authors obtained the schema matching using their scalable ontology alignment algorithm and collected the semantically linked resources, referred to as the Semantic Link Cloud (SLC) collectively for each of the resources. Techniques were addressed to solve several data heterogeneity: value transformation, structural transformation and logical transformation. These experiments show the strength and efficiency of the proposed matching approach.

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