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现代图书情报技术  2014, Vol. 30 Issue (5): 33-40    DOI: 10.11925/infotech.1003-3513.2014.05.05
  知识组织与知识管理 本期目录 | 过刊浏览 | 高级检索 |
面向知识关联的标签云优化机理研究*
毕强1, 周姗姗1, 马志强2, 滕广青2
1 吉林大学管理学院 长春 130022;
2 东北师范大学计算机科学与信息技术学院 长春 130117
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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摘要 

【目的】通过揭示与呈现Folksonomy标签云中的关联关系, 对标签云优化机理进行探讨。【应用背景】 传统Folksonomy知识组织模式中的标签云由于无法体现主题知识之间的关联, 制约了标签云的感知有用性。 【方法】 通过对用户标签网络的属性分析以及模块化处理, 将标签云中的标签划分成若干个知识群落。借助连线、颜色、字号的相互配合, 从主题知识关联的视角优化标签云。【结果】 发现社群中隐含的知识群落具有稳健性, 能够呈现出知识间的关联关系。【结论】面向知识关联的标签云优化能够在多个粒度上提高标签云的感知有用性, 促进更科学实用的标签云体系研发。

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滕广青
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周姗姗
毕强
关键词 Folksonomy知识关联标签云关联标签网络分析    
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.

Key wordsFolksonomy    Knowledge relation    Tag cloud    Related tags    Network analysis
收稿日期: 2014-03-23     
:  G350.7  
基金资助:

*本文系国家自然科学基金项目“语义网络环境下数字图书馆资源多维度聚合与可视化展示研究”(项目编号: 71273111)的研究成果之一

通讯作者: 毕强 E-mail:biqiang12345@163.com   
作者简介: 周珊珊: 采集、清洗数据, 进行实验; 毕强: 提出研究思路; 马志强: 设计研究方案; 滕广青: 论文起草及修订。
引用本文:   
毕强, 周姗姗, 马志强, 滕广青. 面向知识关联的标签云优化机理研究*[J]. 现代图书情报技术, 2014, 30(5): 33-40.
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, DOI:10.11925/infotech.1003-3513.2014.05.05.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.05.05

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