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现代图书情报技术  2015, Vol. 31 Issue (10): 30-39    DOI: 10.11925/infotech.1003-3513.2015.10.05
  专题 本期目录 | 过刊浏览 | 高级检索 |
标注内容与用户属性结合的标签聚类研究
顾晓雪1, 章成志1,2
1 南京理工大学经济管理学院 南京 210094;
2 江苏省数据工程与知识服务重点实验室(南京大学) 南京 210093
Combined with Annotated Content and User Attributes for Tag Clustering
Gu Xiaoxue1, Zhang Chengzhi1,2
1 School of Economics & Management, Nanjing University of Science and Technology, Nanjing 210094, China;
2 Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University), Nanjing 210093, China
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摘要 

[目的] 研究标签聚类中标注内容与用户属性及其结合对聚类效果的影响。[方法] 采用科学网博客数据, 对其进行特征抽取、模型构建和相似度计算, 利用线性函数和Sigmod函数进行相似度加权, 并使用AP聚类算法进行标签聚类。[结果] 在学科分类体系下, 用户属性与标注内容的结合均对标签聚类的结果有所提升, Sigmod加权表现最优; 在系统分类体系下, 两者结合均不如标注内容结果表现优秀。[局限] 选择的数据量较小, 评估标签聚类的分类体系不够完善, AP聚类算法不适用于大数据的处理。[结论] 两种特征的结合在部分情况下能够提高聚类效果, 标签聚类中应更加关注标签的内容特征。

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Abstract

[Objective] Explore the impact of tags' annotated content and tags' user attributes and their combinations in tag clustering. [Methods] Using ScienceNet.cn blogs, extract tag feature, build a vector space model and calculate the similarities between tags where linear method and Sigmod method are used to weight them, finally use the AP algorithm to cluster the tags. [Results] Experimental evaluation results show that in subject classification, in combination of annotated content and user attributes, two types of weighting methods can improve the clustering results, and the performace of Sigmod method is optimal; while in systematic classification, the combination of these two features can't perform as well as the former one and even worse than the content feature. [Limitations] The data selected for experiment is small and the classification for estimating the clustering results is not perfect. What's more, AP clustering algorithm lacks the ability to deal with big data. [Conclusions] The combination of these two features can improve the tag clustering results in some cases, and we should focus more on tag's content in tag clustering.

收稿日期: 2015-04-29     
:  G250  
基金资助:

本文系国家社会科学基金重大项目“面向突发事件应急决策的快速响应情报体系研究”(项目编号:13&ZD174)、国家社会科学基金项目“在线社交网络中基于用户的知识组织模式研究”(项目编号:14BTQ033)和教育部人文社会科学基金规划项目“多语言高质量社会化标签生成及聚类研究”(项目编号:13YJA870020)的研究成果之一。

通讯作者: 章成志, ORCID: 0000-0001-8121-4796, E-mail: zhangcz@njust.edu.cn。     E-mail: zhangcz@njust.edu.cn
作者简介: 作者贡献声明:顾晓雪: 研究方案设计, 实验设计与实施, 数据清洗与分析, 论文起草; 章成志: 提出研究思路, 讨论研究方案, 采集并分析数据, 论文最终版本修订。
引用本文:   
顾晓雪, 章成志. 标注内容与用户属性结合的标签聚类研究[J]. 现代图书情报技术, 2015, 31(10): 30-39.
Gu Xiaoxue, Zhang Chengzhi. Combined with Annotated Content and User Attributes for Tag Clustering. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.10.05.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.10.05

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