Wikipedia2Onto - building concept ontology automatically, experimenting with web image retrieval
|Wikipedia2Onto - building concept ontology automatically, experimenting with web image retrieval|
|Author(s)||Wang H., Jiang X., Chia L.-T., Tan A.-H.|
|Published in||Informatica (Ljubljana)|
|Keyword(s)||Ontology, Semantic concept, Web image classification, Wikipedia (Extra: Association rule mining, Automatic construction, Biomedical informatics, Library science, Local feature, Multi-modality ontology, Ontology construction, Real-world objects, Semantic concept, Training image, Web image retrieval, Web images, Wikipedia, Associative processing, Data mining, Image analysis, Image classification, Image retrieval, Information science, Semantic Web, World Wide Web, Ontology)|
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Wikipedia2Onto - building concept ontology automatically, experimenting with web image retrieval is a 2010 journal article written in English by Wang H., Jiang X., Chia L.-T., Tan A.-H. and published in Informatica (Ljubljana).
Given its effectiveness to better understand data, ontology has been used in various domains including cartificial intelligence, biomedical informatics and library science. What we have tried to promote is the use of ontology to better understand media (in particular, images) on the World Wide Web. This paper describes our preliminary attempt to construct a large-scale multi-modality ontology, called AutoMMOnto, for web image classification. Particularly, to enable the automation of text ontology construction, we take advantage of both structural and content features of Wikipedia and formalize real world objects in terms of concepts and relationships. For visual part, we train classifiers according to both global and local features, and generate middle-level concepts from the training images. A variant of the association rule mining algorithm is further developed to refine the built ontology. Our experimental results show that our method allows automatic construction of large-scale multi-modality ontology with high accuracy from challenging web image data set.
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