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New Technology of Library and Information Service  2014, Vol. 30 Issue (5): 33-40    DOI: 10.11925/infotech.1003-3513.2014.05.05
Study on Optimization Mechanism of Tag Cloud for Knowledge Relation
Bi Qiang1, Zhou Shanshan1, Ma Zhiqiang2, Teng Guangqing2
1 School of Management, Jilin University, Changchun 130022, China;
2 School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
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[Objective] This article explores the optimization mechanism of tag cloud by the revealing and presenting of relationship of tag cloud in folksonomy. [Context] The traditional mode of knowledge organization of tag cloud in folksonomy is unable to reflect the knowledge relevance between the themes, which restricts the perceived usefulness of tag cloud. [Methods] Through the analysis of attribute on network of user tags and modular processing, tags in cloud are divided into a number of knowledge communities. With the cooperation among the links, the color, font size, tag cloud is optimized from the perspective of knowledge relevance between the themes. [Results] The latent knowledge community is robust, and it is able to show the relationship between knowledge. [Conclusions] Optimization of tag cloud based on knowledge relevance can improve perceived usefulness on multiple granularities, and promote the researching and developing of more scientific and practical tag cloud system.

Key wordsFolksonomy      Knowledge relation      Tag cloud      Related tags      Network analysis     
Received: 23 March 2014      Published: 06 June 2014
:  G350.7  

Cite this article:

Bi Qiang, Zhou Shanshan, Ma Zhiqiang, Teng Guangqing. Study on Optimization Mechanism of Tag Cloud for Knowledge Relation. New Technology of Library and Information Service, 2014, 30(5): 33-40.

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[1] Peters I, Stock W G. Folksonomy and Information Retrieval[J]. Proceedings of the American Society for Information Science and Technology, 2007, 44(1): 1-28.
[2] 滕广青, 毕强, 高娅. 基于概念格的Folksonomy知识组织研究: 关联标签的结构特征分析[J]. 现代图书情报技术, 2012(6): 22-28. (Teng Guangqing, Bi Qiang, Gao Ya. A Study on Knowledge Organization of Folksonomy Based on Concept Lattice: Analysis on Structural Characteristics of Related Tags[J]. New Technology of Library and Information Service, 2012(6): 22-28.)
[3] Albert R, Barabási A L. Statistical Mechanics of Complex Networks[J]. Reviews of Modern Physics, 2002, 74(1): 47-97.
[4] Schmitz C, Hotho A, Jäschke R, et al. Mining Association Rules in Folksonomies[C]. In: Proceedings of the 10th IFCS Conference on Data Science and Classification. Heidelberg, Berlin: Springer-Verlag, 2006: 261-270.
[5] Begelman G, Keller P, Smadja F. Automated Tag Clustering: Improving Search and Exploration in the Tag Space[EB/OL]. [2014-04-13].
[6] Cattuto C, Benz D, Hotho A, et al. Semantic Grounding of Tag Relatedness in Social Bookmarking Systems[C]. In: Proceedings of the 7th International Conference on the Semantic Web. Heidelberg, Berlin: Springer-Verlag, 2008: 615-631.
[7] Kawakubo H, Akima Y, Yanai K. Automatic Construction of A Folksonomy-Based Visual Ontology[C]. In: Proceedings of 2010 IEEE International Symposium on Multimedia (ISM). Washington, DC: IEEE Computer Society, 2010: 330-335.
[8] Solskinnsbakk G, Gulla J A. A Hybrid Approach to Constructing Tag Hierarchies[C]. In: Proceedings of the 2010 International Conference on On the move to Meaningful Internet Systems(OTM'10). Heidelberg, Berlin: Springer, 2010: 975-982.
[9] Zhitomirsky-Geffet M, Bar-Ilan J, Miller Y, et al. A Generic Framework for Collaborative Multi-Perspective Ontology Ac-quisition[J]. Online Information Review, 2010, 34(1): 145-159.
[10] 王翠英. 标签的聚类分析研究[J]. 现代图书情报技术, 2008(5): 67-71. (Wang Cuiying. Study on Tag Clustering Analysis[J]. New Technology of Library and Information Service, 2008(5): 67-71.)
[11] 周鑫, 王军. 基于概念外延的Folksonomy语义关系挖掘方法[J]. 现代图书情报技术, 2008(10): 6-10. (Zhou Xin, Wang Jun. Semantic Relations Mining in Folksonomy Based on Extensions of Concepts[J]. New Technology of Library and Information Service, 2008(10): 6-10.)
[12] 刘海旭, 郑岩. 基于语义的标签关联算法[J]. 软件, 2012, 33(12): 136-138. (Liu Haixu, Zheng Yan. A Tag Relevance Algorithm Based on Semantic[J]. Software, 2012, 33(12): 136-138.)
[13] Chojnacki S, Klopotek M. Random Graph Generative Model for Folksonomy Network Structure Approximation[J]. Procedia Computer Science, 2012, 1(1): 1683-1688.
[14] Mas M D. Intelligent Interface Architectures for Folksonomy Driven Structure Network[C]. In: Proceedings of the 6th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). Washington, DC: IEEE Computer Society, 2012: 519-525.
[15] 吴江. 自由分类标签类聚成网状分类结构研究与实现[J]. 图书情报知识, 2011(1): 75-81. (Wu Jiang. Research and Implementation of Folksonomy Tags Clustering into Network Catalogue Map[J]. Document, Information & Knowledge, 2011(1): 75-81.)
[16] 滕广青, 毕强. 领域本体与社群分类法结构中心性的比较研究[J]. 情报学报, 2013, 32(10): 1037-1045. (Teng Guangqing, Bi Qiang. Comparative Study on Structural Centrality of Domain Ontology and Folksonomy[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(10): 1037-1045.)
[17] Bastian M, Heymann S, Jacomy M. Gephi: An Open Source Software for Exploring and Manipulating Networks[EB/OL]. [2013-12-03]. paper/viewFile/154Forum/1009.
[18] Jacomy M, Heymann S, Venturini T, et al. ForceAtlas2, A Continuous Graph Layout Algorithm for Handy Network Visualization[EB/OL]. [2013-12-03]. http://www.medialab.
[19] Blondel V D, Guillaume J L, Lambiotte R, et al. Fast Unfolding of Communities in Large Networks[EB/OL]. [2013-12-03]. 13.pdf.
[20] Fruchterman T M J, Reingold E M. Graph Drawing by Force-directed Placement[J]. Software: Practice and Experience, 1991, 21(11): 1129-1164.

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