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New Technology of Library and Information Service  2010, Vol. 26 Issue (7/8): 58-65    DOI: 10.11925/infotech.1003-3513.2010.07-08.11
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Research on Retrieval and Reasoning of Ultra-Large-Scale OntoThesaurus
Zeng Xinhong1,2  Huang Huajun2  Lin Weiming1
1(Shenzhen University Library, Shenzhen 518060, China)
2(College of Computer and Software, Shenzhen University,Shenzhen 518060, China)
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This paper makes a research on the implementation of network-based retrieval and reasoning about Ultra-Large-Scale OntoThesaurus, and the proposed solution has successfully applied to the realization of the CCT1_OTCSS, which is a co-construction and sharing system of an Ultra-Large-Scale Ontology named CCT1_OntoThesaurus. This paper proposes the structure of Lucene index based on the idea of triple “subject, predicate, object” of the RDF, and validates the feasibility of implementing efficient retrieval, terminology services and reasoning based on the Lucene index of Ultra-Large-Scale OntoThesaurus. The solution can be reused for several Ultra-Large-Scale Chinese thesauri most widely used in China at present, implementing quickly Ontology-oriented upgrading, networked co-construction, sharing and dynamic updating for them, and also has a reference value for other large-scale knowledge organization systems (thesauri, Ontology, etc.) in the form of XML, RDF or OWL at home and abroad.

Key wordsLucene      Ultra-Large-Scale OntoThesaurus      Ontology retrieval      Reason      Index     
Received: 29 June 2010      Published: 19 September 2010



Corresponding Authors: Zeng Xinhong     E-mail:
About author:: Zeng Xinhong Huang Huajun Lin Weiming

Cite this article:

Zeng Xinhong Huang Huajun Lin Weiming. Research on Retrieval and Reasoning of Ultra-Large-Scale OntoThesaurus. New Technology of Library and Information Service, 2010, 26(7/8): 58-65.

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[[1] 曾新红,明仲,蒋颖,等.中文叙词表本体共建共享系统研究[J].情报学报, 2008,27(3):386-394.
[2] Jena – A Semantic Web Framework for Java[EB/OL].[2008-05-22].
[3] W3C.SPARQL Query Language for RDF[EB/OL]. [2008-12-24].
[4] 曾新红. 中文叙词表本体——叙词表与本体的融合[J]. 现代图书情报技术,2009(1):34-43.
[5] 曾新红,林伟明,明仲.中文叙词表本体的检索实现及其术语学服务研究[J].现代图书情报技术,2008(2):8-13.
[6] 曾新红,林伟明,明仲.中文叙词表本体一致性检测机制研究与实现[J].现代图书情报技术,2008(5):1-9.
[7] 李新叶,苑津莎.一种快速的XML语义检索算法[J].电子学报,2007,35(11):2220-2225.
[8] 孔令波,唐世渭,杨冬青,等.XML数据的查询技术[J].软件学报,2007,18(6):1400-1418.
[9] 孔令波,唐世渭,杨冬青,等.XML数据索引技术[J].软件学报,2005,16(12):2063-2079.
[10] 邵晓宇.基于本体的大型数据资源智能检索研究[D].合肥:合肥工业大学,2008:10-11.
[11] 汪智勇.本体查询与推理研究及其实现[D].长沙:中南大学,2007:7-39.
[12] 吴元业.基于信任度的个性化推理机的研究与实现[D].深圳:深圳大学,2009:8-14.
[13] 推理机Jess、Racer、Jena 比较[EB/OL]. [2009-04-11].
[14] LUCENE.COM.CN 中国[EB/OL]. [2008-10-26].
[15] 陆建江,张亚非,苗壮,等.语义网原理与技术[M].北京:科学出版社,2007:136-139.
[16] CCT1_OTCSSOntoThesaurus-TS.中文叙词本体共建共享系统[EB/OL]. [2010-06-03].
[17] CCT1_OTCSSOntoThesaurus-API[EB/OL]. [2010-06-03]. http://
[18] 宋炜,张铭.语义网简明教程[M].北京:高等教育出版社,2004:23-139.
[19] 林伟明,曾新红. OntoThesaurus Web Service API及其应用研究[J]. 图书情报工作,2010,54(2):119-139.

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