图书情报工作

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2013 , Vol. 57 >Issue 22: 120 - 125,59

DOI: https://doi.org/10.7536/j.issn.0252-3116.2013.22.019

知识组织

从大众分类到层次式资源组织体系——利用聚类信息构建标签树

  • 罗鹏程 ,
  • 陈翀
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  • 北京师范大学信息管理
罗鹏程，北京师范大学信息管理系硕士研究生

收稿日期: 2013-10-08

  修回日期: 2013-11-04

  网络出版日期: 2013-11-20

基金资助

本文系国家自然科学基金项目“异构'非网页'资源的组织与融合方法研究”（项目编号：70903008）和国家科技支撑计划项目“文化资源服务平台解决方案及标准研究”的子课题“文化资源数字化建设规范与标准研究”（项目编号：2012BAH01F01-03）研究成果之一。

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Resource Organization Systems from Folksonomy to Hierarchical：Constructing the Tag Tree by Exploiting Clustering Information

  • Luo Pengcheng ,
  • Chen Chong
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  • Department of Information Management, Beijing Normal University, Beijing 100875

Received date: 2013-10-08

  Revised date: 2013-11-04

  Online published: 2013-11-20

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摘要

提出一种合理运用大众标注数据自动构建资源导航体系的方法，首先使用聚类技术将标签分成主题相近的簇，然后在簇内按标签的泛指度和相似性构造标签树。将该方法用在公开的标签数据集Social-ODP-2k9上进行实效评价，结果表明，本方法减少了以往标签树构造方法中“意义漂移”的缺陷，使组织结构有更好的语义一致性，且具有更好的导航效率，从而使本研究能适应信息组织与服务的实际需要。

关键词： 社会标注; 标签树; 资源组织; 导航

本文引用格式

罗鹏程 , 陈翀 . 从大众分类到层次式资源组织体系——利用聚类信息构建标签树[J]. 图书情报工作, 2013 , 57(22) : 120 -125,59 . DOI: 10.7536/j.issn.0252-3116.2013.22.019

Abstract

This paper proposes a new approach to automatically construct the resource navigation system with taxonomy from social annotation. Tags are first clustered to groups according to topics through clustering technology; then a sub-tree is constructed according to tags' similarity and their generality degree in each group. The open Social-ODP-2k9 tags dataset are experimented on to make effectiveness evaluation. The method overcomes the shortcoming of semantic-drift in the existing method, and improves the semantic consistency of information organization and efficiency of navigation, to meet the new practical need for information organization and services.

Key words： social annotation; tag tree; resource organization; navigation

参考文献

[1] Ramage D, Heymann P, Manning C D, et al. Clustering the tagged Web[C]//Proceedings of the Second ACM International Conference on Web Search and Data Mining. New York: ACM, 2009: 54-63.
[2] Zubiaga A, Martínez R, Fresno V. Getting the most out of social annotations for Web page classification[C]//Proceedings of the 9th ACM Symposium on Document Engineering. New York: ACM, 2009: 74-83.
[3] Bao Shenghua, Wu Xiaoyuan, Fei Ben, et al. Optimizing Web search using social annotations[C]//Proceedings of the 16th International Conference on World Wide Web. New York: ACM, 2007: 501-510.
[4] Heymann P, Koutrika G, Garcia-Molina H. Can social bookmarking improve Web search?[C]//Proceedings of the International Conference on Web Search and Web Data Mining. New York: ACM, 2008: 195-206.
[5] Begelman G, Keller P, Smadja F. Automated tag clustering: Improving search and exploration in the tag space[C]//Collaborative Web Tagging Workshop at WWW2006. New York: ACM, 2006: 15-33.
[6] Cui Jianwei, Liu Hongyan, He Jun, et al. TagClus:A random walk-based method for tag clustering[J]. Knowledge and Information Systems, 2011, 27(2): 193-225.
[7] Laniado D, Eynard D, Colombetti M. Using WordNet to turn a folksonomy into a hierarchy of concepts[C]//Proceedings of 4th Italian Semantic Web Workshop.Bari:University of Bari, 2007: 192-201.
[8] Lin H, Davis J, Zhou Ying. An integrated approach to extracting ontological structures from folksonomies[M]//The Semantic Web:Research and Applications. Berlin:Springer, 2009: 654-668.
[9] Heymann P, Garcia-Molina H. Collaborative creation of communal hierarchical taxonomies in social tagging systems[R/OL].[2013-05-26]. http://ilpubs.stanford.edu:8090/775/?auth=basic.
[10] Song Yang, Qiu Baojun, Farooq U. Hierarchical tag visualization and application for tag recommendations[C]//Proceedings of the 20th ACM International Conference on Information and Knowledge Management. New York: ACM, 2011: 1331-1340.
[11] Zhou Mianwei, Bao Shenghua, Wu Xian, et al. An unsupervised model for exploring hierarchical semantics from social annotations[C]// Proceedings of the 6th International Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference. Berlin: Springer, 2007: 680-693.
[12] Strohmaier M, Helic D, Benz D, et al. Evaluation of folksonomy induction algorithms[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3(4): 74.
[13] Plangprasopchok A, Lerman K, Getoor L. Growing a tree in the forest: Constructing folksonomies by integrating structured metadata[C]//Proceedings of the 16th ACM International Conference on Knowledge Discovery and Data mining. New York: ACM, 2010: 949-958.
[14] Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. New York: ACM, 1996: 226-231.
[15] Newman M E J. Fast algorithm for detecting community structure in networks[J]. Physical Review E, 2004, 69(6): 066133-1-066133-5.
[16] 陈翀, 罗鹏程, 刘晓兵, 等. 网络资源层次组织规律定量研究[J]. 中国图书馆学报, 2012 (6): 72-80.
[17] Social-ODP-2k9数据集 [EB/OL]. [2013-11-08]. http://nlp.uned.es/social-tagging/socialodp2k9/.

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