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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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Abstract [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.
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Received: 23 March 2014
Published: 06 June 2014
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