Wikipedia2Onto: Building concept ontology automatically, experimenting with web image retrieval
Given its effectiveness to better understand data, ontology has been used in various domains including artificial 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. T...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/5198 https://ink.library.smu.edu.sg/context/sis_research/article/6201/viewcontent/304_371_1_PB.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Given its effectiveness to better understand data, ontology has been used in various domains including artificial 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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