Everything You Wanted to Know About MPEG-7: Part 1
IEEE MultiMedia
Everything You Wanted to Know About MPEG-7: Part 2
IEEE MultiMedia
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
A reduced yet extensible audio-visual description language
Proceedings of the 2004 ACM symposium on Document engineering
Interoperability Support between MPEG-7/21 and OWL in DS-MIRF
IEEE Transactions on Knowledge and Data Engineering
An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size
Web Semantics: Science, Services and Agents on the World Wide Web
Ontology Alignment: Bridging the Semantic Gap
Ontology Alignment: Bridging the Semantic Gap
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Semantic annotation of images and videos for multimedia analysis
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Automatic alignment of ontology eliminating the probable misalignments
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Enhancing the semantic interoperability of multimedia through a core ontology
IEEE Transactions on Circuits and Systems for Video Technology
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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.