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New Technology of Library and Information Service  2006, Vol. 1 Issue (9): 34-37    DOI: 10.11925/infotech.1003-3513.2006.09.08
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A Text Semantic Information Processing Method Based on Ontology and Latent Semantic Indexing
Qin Chunxiu    Liu Huailiang    Zhao Pengwei
 (School of Economics and Management, Xidian University， Xi’an 710071,China)
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Abstract  

This paper presents a method for text semantic information processing based on Ontology and latent semantic indexing. Firstly, virtual standard text characteristic vectors are constructed; then, the texts are semantically classified into document sets according to virtual standard text characteristic vectors by using latent semantic indexing method; finally, semantically explicit annotations to the document sets are abtained from Ontology-base by guidance of virtual standard text characteristic vectors. Experiments show that method can achieve good text clustering of semantic level, and the clustering can explicitly indicate categories of the clustered documents.
Key words： Latent semantic indexing      Ontology      Clustering      Semantics      Annotation     
Received: 20 June 2006      Published: 25 September 2006
ZTFLH: 

G354.2

 		 
Corresponding Authors: Qin Chunxiu     E-mail: qinchx@126.com
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Cite this article:

Qin Chunxiu,Liu Huailiang,Zhao Pengwei . A Text Semantic Information Processing Method Based on Ontology and Latent Semantic Indexing. New Technology of Library and Information Service, 2006, 1(9): 34-37.

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http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2006.09.08     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2006/V1/I9/34

1张晓林.Semantic Web与基于语义的网络信息检索.情报学报，2002，21(4):413-420
2Berry M W, Dumais S T, O. brien G W. Using linear algebra for intelligent information retrieval, SIAM Review, 1995, 37(4):573-595
3Deerwester S, Dumais S T, Furnas G W et al.Indexing by Latent Semantic Analysis， Journal of the American Society for Information Science, 1990, 41(6):391-407
4Neches R, Fikes R E, Gruber T R, et al. Enabling Technology for Knowledge Sharing.AI Magazine, 1991, 12(3):36-56
5W. N. Borst. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis, University of Twente, Enschede, 1997
6林鸿飞，姚天顺.基于潜在语义索引的文本浏览机制.中文信息学报, 2000, 14(5):49-56
7杨梁彬.文本检索的潜在语义索引法初探.大学图书馆学报,2003(6):68-74,84
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