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New Technology of Library and Information Service  2014, Vol. 30 Issue (4): 7-13    DOI: 10.11925/infotech.1003-3513.2014.04.02
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Analysis and Experimental Research on Method of Semantic Knowledge Acquisition for Institutional Repository
Wang Sili, Zhu Zhongming, Yao Xiaona
The Lanzhou Branch of National Science Library, Chinese Academy of Sciences, Lanzhou 730000, China
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Abstract  

[Objective] The paper proposes and forms an effective method of semantic knowledge acquisition through analysis, summary and experiment, in order to provide theoretical principle and possible technological route for the semantization of Institutional Repository. [Methods] Based on the contrastive analysis of methods of semantic knowledge acquisition both at home and abroad, the paper proposes a system framework of semantic knowledge acquisition for Institutional Repository, and sums up its key technologies for deep analysis and then takes the CAS IR GRID for an experimental study. [Results] This method can automatically and effectively acquire semantic knowledge information from data and entity relationship structure of relational database of underlying Institutional Repository and convert it into RDF triples for browse and search. [Limitations] To define a reasonable and effective mapping rule may need domain expert evaluation, more manual intervention and repeated experiments. The semantic knowledge acquisition and relevance study for the same entity object between different Institutional Repository is not involved in this paper. [Conclusions] This study may better help follow-up researchers and developers quickly understand and master the method and key technologies of semantic knowledge acquisition, then lay the foundations for enhancing knowledge service capabilities of Institutional Repository.

Key wordsInstitutional Repository      Semantic mapping      Knowledge acquisition      ER mode     
Received: 13 November 2013      Published: 19 May 2014
:  G250  

Cite this article:

Wang Sili, Zhu Zhongming, Yao Xiaona. Analysis and Experimental Research on Method of Semantic Knowledge Acquisition for Institutional Repository. New Technology of Library and Information Service, 2014, 30(4): 7-13.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2014.04.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2014/V30/I4/7

[1] 董金祥.基于语义面向服务的知识管理与处理[M].杭州:浙江大学出版社,2009:111-115.(Dong Jinxiang.Semantics-Based Services-Oriented Knowledge Management and Proce­ssing[M].Hangzhou:Zhejiang University Press,2009:111-115.)
[2] 候筱婷.基于数据仓库、OLAP和数据挖掘技术的数据分析、展现与预测[D].西安:西安电子科技大学,2007.(Hou Xiaoting.Data Analysis,Exhibition and Prediction Based on Data Warehouse,OLAP and Data Mining Technologies[D].Xi'an:Xidian University,2007.)
[3] Hammer J,McHugh J,Garcia-Molina H.Semistructured Data:the TSIMMIS Experience[C].In:Proceedings of the 1st East-European Workshop on Advances in Database and Information Systems(ADBI'97).UK:British Computer Society Swinton,1997:22-30.
[4] Soderland S.Learning Information Extraction Rules for Semi-Structured and Free Text[J].Machine Learning,1999,34(1-3):233-272.
[5] Etzioni O,Cafarella M,Downey D,et al.Unsupervised Named-Entity Extraction from the Web:An Experimental Study[J].Artificial Intelligence,2005,165(1):91-134.
[6] Ashraf F,Alhajj R.CluxTex:Information Extraction from HTML Pages[C].In:Proceedings of the IEEE 21st International Conference on Advanced Information Networking and Applications Workshops.Niagara Falls:IEEE,2007:355-360.
[7] Cheng C K,Pan X S,Kurfess F.Ontology-based Semantic Classification of Unstructured Documents[C].In:Proceedings of the 1st International Workshop on AMR 2003.2004:120-131.
[8] Khasawneh N,Chan C C.Active User-based and Ontology-based Web Log Data Preprocessing for Web Usage Mining[C].In:Proceeding of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence.Hong Kong,China:IEEE,2006:325-328.
[9] Volz R,Handschuh S,Staab S,et al.Unveiling the Hidden Bride:Deep Annotation for Mapping and Migrating Legracy Data to the Semantic Web[J].Journal of Web Semantics,2004,11(1):187-206.
[10] Astroval I.Reverse Engineering of Relational Databases to Ontologies[C].In:Proceedings of the 1st European Semantic Web Symposium.Berlin:Springer,2004:327-341.
[11] Xu Z M,Zhang S C,Dong Y S.Mapping between Relational Database Schema and OWL Ontology for Deep Annotation[C].In:Proceeding of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence.Hong Kong,China:IEEE,2006:548-552.
[12] Bizer C,Cyganiak R.D2R Server-publishing Relational Databases on the Semantic Web[C].In:Proceedings of the 5th International Semantic Web Conference.2006.
[13] R2RML:RDB to RDF Mapping Language[EB/OL].(2012-09-27).[2013-07-10].http://www.w3.org/TR/r2rml/.
[14] 中国科学院机构知识库服务网格[EB/OL].[2013-08-01].http://www.irgrid.ac.cn/.(CAS IR GRID[EB/OL].[2013-08-01].http://www.irgrid.ac.cn/.)
[15] DataMaster[EB/OL].[2013-06-20].http://eulergui.sourceforge.net/documentation.html.

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