A framework for integrating DBpedia in a multi-modality ontology news image retrieval system
knowledge sharing communities like Wikipedia and automated extraction like DBpedia enable a large construction of machine processing knowledge bases with relational fact of entities. These options give a great opportunity for researcher to use it as a domain concept between low-level features and hi...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/30417/1/05995779.pdf http://irep.iium.edu.my/30417/ http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5995779&tag=1 |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English |
| Summary: | knowledge sharing communities like Wikipedia and automated extraction like DBpedia enable a large construction of machine processing knowledge bases with relational fact of entities. These options give a great opportunity for researcher to use it as a domain concept between low-level features and high level concepts for image retrieval. The collection of images attached to entities, such as on-line news articles with images, are abundant on the Internet. Still, it is difficult to retrieve accurate information on these entities. Using entity names in a search engine yields large lists, but often results in imprecise and
unsatisfactory outcomes. Our goal is to populate a knowledge base with on-line image news resources in the BBC sport domain. This system will yield high precision, a high recall and include diverse sports photos for specific entities. A multi-modality ontology retrieval system, with
relational facts about entities for generating expanded queries, will be used to retrieve results. DBpedia will be used as a domain sport ontology description, and will be integrated with a textual description and a visual description, both generated by hand. To overcome semantic interoperability between ontologies,automated ontology alignment is used. In addition, visual similarity measures based on MPEG7 descriptions and SIFT features, are used for higher diversity in the final rankings. |
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