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New Technology of Library and Information Service  2007, Vol. 2 Issue (8): 80-83    DOI: 10.11925/infotech.1003-3513.2007.08.19
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Design and Implementation of Elementary Education Yellow Page Website Auto-generation System
Luo Liqun1,2   Zhang Wei1,2   Chen Jinxin1,2
1(Department of Education Technology,Nanjing Normal University,Nanjing  210097,China)
2(Lab of Web Data Mining,Nanjing Normal University,Nanjing  210097,China)
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In order to solve the problems that education users have difficulty in getting resources and straightly link to the elementary education Websites, this paper mainly discusses the core technology of automatic finding and classification of elementary education resources and Websites.This paper also discusses heuristic rules and feather muli-selection method to find Deep Web.The prototype system using technologies mentioned above provides the education users with many classification ways for search.

Key wordsYellow page Website      Auto-generation system      Auto-classification      Vertical search      Deep Web     
Received: 13 June 2007      Published: 25 August 2007


Corresponding Authors: Luo Liqun     E-mail:
About author:: Luo Liqun,Zhang Wei,Chen Jinxin

Cite this article:

Luo Liqun,Zhang Wei,Chen Jinxin. Design and Implementation of Elementary Education Yellow Page Website Auto-generation System. New Technology of Library and Information Service, 2007, 2(8): 80-83.

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[1] 李伟超,王兰敬.论搜索引擎的工作机制和发展趋势[J]现代情报,2002,22(12):107-108
[2] Sun A X,Lim E P.Hierarchical Text Classification and Evaluation[A].In:Proceedings of the 2001 IEEE International Conference on DataMining[C].California,USA,2001.521 - 528.
[3] IWAYAMA M.A Comparison of Category Search Strategies.In:ACM Conference on Research and Development on Information,Washington[C],1995.
[4] Yang Y M.An Evaluation of Statistical Approaches to Text Categorization[J].Journal of Information Retrieval,1999,1 (2):67 - 88.
[5] Gordon M,Pathak P.Finding Information on the World Wide Web:the Retrieval Effectiveness of Search Engines[J].Information Processing and Management,1999,35 (2):141-180.
[6] 肖雪,何中市.基于向量空间模型的中文文本层次分类方法研究[J]计算机应用,2006(5):1125-1126
[7] Fan R E,Chen P H,Lin C J.Working Set Selection Using the Second Order Information for Training SVM[J].Journal of Machine Learning Research,2005(6):1889 - 1918.
[8] 张学工关于统计学习理论与支持向量机[J].自动化学报,2000,26( 1):32-42
[9] Vapnik V,Golowich S,Smola A,Support Vector Method for Function Approximation,Regression Estimation,and Signal Processing[C].Mozer M,Jordan M,Petsche T.Neural Information Processing Systems.Cambridge: MIT Press,1997:281-287
[10] Cortes C,Vapnik V.Support-vector Networks[J].Machine Learning,1995(20):273-297
[11] 杨晓江,李丽娟,田俊华,等.面向基础教育的Web资源垂直服务体系研究[J]中国远程教育,2006(7):53-57.

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