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Wikipedia2Onto - building concept ontology automatically, experimenting with web image retrieval
Abstract Given its effectiveness to better understaGiven 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.uracy from challenging web image data set.
Abstractsub Given its effectiveness to better understaGiven 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.uracy from challenging web image data set.
Bibtextype article  +
Has author Haofen Wang + , Xing Jiang + , Chia L.-T. + , Tan A.-H. +
Has extra keyword 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 +
Has keyword Ontology + , Semantic concept + , Web image classification + , Wikipedia +
Issn 3505596  +
Issue 3  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 297–306  +
Published in Informatica (Ljubljana) +
Title Wikipedia2Onto - building concept ontology automatically, experimenting with web image retrieval +
Type journal article  +
Volume 34  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 8 November 2014 07:40:39  +
Categories Publications without license parameter  + , Publications without DOI parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 07:40:39  +
DateThis property is a special property in this wiki. 2010  +
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