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Resource Organization Systems from Folksonomy to Hierarchical：Constructing the Tag Tree by Exploiting Clustering Information
Received date: 2013-10-08
Revised date: 2013-11-04
Online published: 2013-11-20
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
Luo Pengcheng , Chen Chong . Resource Organization Systems from Folksonomy to Hierarchical：Constructing the Tag Tree by Exploiting Clustering Information[J]. Library and Information Service, 2013 , 57(22) : 120 -125,59 . DOI: 10.7536/j.issn.0252-3116.2013.22.019
[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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