Please wait a minute...
Advanced Search
现代图书情报技术  2010, Vol. 26 Issue (3): 27-32     https://doi.org/10.11925/infotech.1003-3513.2010.03.05
  知识组织与知识管理 本期目录 | 过刊浏览 | 高级检索 |
基于网络协作标注的标签消歧方法述评
窦玉萌
(首都图书馆 北京 100021)
Review on Tag Meaning Disambiguation Methods Based on Web Collaborative Tagging
Dou Yumeng
(Capital Library of China, Beijing 100021, China)
全文: PDF (595 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

以网络协作标注中的标签为研究对象,调研标签消歧方法,并将其划分为基于数据挖掘方法消歧、基于统计分析方法消歧、利用相关知识组织工具消歧、引入控制机制消歧和开发可视化组件消歧5类。比较这5类消歧方法在用户参与度、消歧时机、消歧性质、实验与应用情况和发展前景5个方面存在的区别和联系。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
窦玉萌
关键词 网络协作标注标签消歧知识组织聚类概率模型    
Abstract

This paper concerns on tags of Web collaborative tagging and mainly researches on tag meaning disambiguation methods, which are classified into five types:data mining method, statistical method, knowledge organization tools method, control mechanisms method and visualization components method. The five methods are compared in five aspects of users’ participation, disambiguation occasion, disambiguation property, experiment and application, as well as the development prospect.

Key wordsWeb collaboration tagging    Tag meaning disambiguation    Knowledge management    Clustering    Probabilistic model
收稿日期: 2010-03-04      出版日期: 2010-03-25
: 

G350

 
通讯作者: 窦玉萌     E-mail: andydym@gmail.com
作者简介: 窦玉萌
引用本文:   
窦玉萌. 基于网络协作标注的标签消歧方法述评[J]. 现代图书情报技术, 2010, 26(3): 27-32.
Dou Yumeng. Review on Tag Meaning Disambiguation Methods Based on Web Collaborative Tagging. New Technology of Library and Information Service, 2010, 26(3): 27-32.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2010.03.05      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2010/V26/I3/27

[1] Zauder K, Lazic J L, Zorica M B. Collaborative Tagging Supported Knowledge Discovery[C]. In: Proceedings of the 29th International Conference on Information Technology Interfaces,Cavtat, Croatia.2007: 437-442.
[2] 韩家炜. 数据挖掘: 概念与技术[M]. 北京: 机械工业出版社, 2007: 3-5.
[3] Mika P. Ontologies are Us: A Unified Model of Social Networks and Semantics[J]. Journal of Web Semantics, 2007,5(1): 5-15.
[4] Yeung C M A, Gibbins N, Shadbolt N. Web Search Disambiguation by Collaborative Tagging [EB/OL]. (2008-03-30). [2008-04-24]. http://eprints.ecs.soton.ac.uk/15393/1/ecir2008_paper.pdf.
[5] Hamasaki M, Matsuo Y, Nishimura T, et al. Ontology Extraction by Collaborative Tagging with Social Networking[EB/OL]. (2008-03-25). [2008-04-24].http://ymatsuo.com/papers/www2008hama.pdf.
[6] Gernmell J, Shepitsen A, Mobasher B, et al. Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering[C]. In: Proceedings of Data Warehousing and Knowledge Discovery. Berlin: Springer-Verlag, 2008:196-205.
[7] Nauman M, Khan S. Using Personalized Web Search for Enhancing Common Sense and Folksonomy Based Intelligent Search Systems[C]. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence. California: IEEE Press, 2007: 423-426.
[8] 张伟. 基础统计学[M]. 西安: 西北大学出版社, 2003: 5.
[9] Weinberger K, Slaney M, Zwo R. Resolving Tag Ambiguity [EB/OL]. [2008-12-20].http://research.yahoo.com/files/ctfp6043-weinberger.pdf.
[10] Zhang L, Wu X, Yu Y. Emergent Semantics from Folksonomies:A Quantitative Study[J]. Journal on Data Semantics VI, 2006(6): 168-186.
[11] Lee S S, Yong H S. TagPlus: A Retrieval System Using Synonym Tag in Folksonomy[C]. In: Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering. New York: IEEE Press, 2007: 294-298.
[12] Lee S S, Yong H S.Component Based Approach to Handle Synonym and Polysemy in Folksonomy[C]. In: Proceedings of the 7th IEEE International Conference on Computer and Information Technology. California: IEEE Press, 2007: 200-205.
[13]Wikipedia[EB/OL].[2009-03-12].http://zh.wikipedia.org/w/index.php?title=Wikipedia:%E5%85%B3%E4%BA%8E&variant=zh-tw.
[14] 王刚. 自动抽取维基百科文本中的语义关系[D]. 上海: 上海交通大学, 2008.
[15] Al-Khalifa H S, Davis H C. FAsTA: A Folksonomy-Based Automatic Metadata Generator [C]. In: Proceedings of 2nd European Conference on Technology Enhanced Learning, Crete, Greece. 2007:414-419.
[16] Sabou M, Gracia J, Angeletou S, et al. Evaluating the Semantic Web:A Task-based Approach[C]. In: Proceedings of Semantic Web. Berlin: Springer-Verlag, 2007:423-437.
[17] Stojanovic L, Stojanovic N, Ma J. An Approach for Combining Ontology Learning and Semantic Tagging in the Ontology Development Process eGovernment Use Case[C]. In: Proceedings of 8th International Conference on Web Information Systems Engineering. Berlin: Springer-Verlag, 2007: 249-260.
[18] Marchetti A, Tesconi M, Ronzanona F, et al. Semkey: A Semantic Collaborative Tagging System [EB/OL]. (2007-03-12). [2008-04-24].http://www2007.org/workshops/paper_45.pdf.
[19] Facetious[EB/OL]. [2008-04-24]. http://www.siderean.com/delicious/facetious.jsp.
[20] Knowledge Hunter. Faceted Browsing: FAC.ETIO.US[EB/OL]. (2007-01-08). [2009-03-12].  http://knowledge-hunter.blogspot.com/2007/01/facetted-browsing-facetious.html.
[21] Graph Del.icio.us Related Tags[EB/OL]. [2009-03-12].http://hublog.hubmed.org/archives/001049.html.
[22] Graph Del.icio.us Related Tags[EB/OL]. [2009-03-12].http://www.hubmed.org/touchgraphs/deltags.php?start=fellowship.

[1] 王若琳, 牛振东, 蔺奇卡, 朱一凡, 邱萍, 陆浩, 刘东磊. 基于异质信息嵌入与RNN聚类参数预测的作者姓名消歧方法*[J]. 数据分析与知识发现, 2021, 5(8): 13-24.
[2] 王晰巍,贾若男,韦雅楠,张柳. 多维度社交网络舆情用户群体聚类分析方法研究*[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[3] 卢利农,祝忠明,张旺强,王小春. 基于Lingo3G聚类算法的机构知识库跨库知识整合与知识指纹服务实现[J]. 数据分析与知识发现, 2021, 5(5): 127-132.
[4] 张梦瑶, 朱广丽, 张顺香, 张标. 基于情感分析的微博热点话题用户群体划分模型 *[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[5] 丁浩, 艾文华, 胡广伟, 李树青, 索炜. 融合用户兴趣波动时序的个性化推荐模型*[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[6] 杨辰, 陈晓虹, 王楚涵, 刘婷婷. 基于用户细粒度属性偏好聚类的推荐策略*[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[7] 于丰畅,程齐凯,陆伟. 基于几何对象聚类的学术文献图表定位研究[J]. 数据分析与知识发现, 2021, 5(1): 140-149.
[8] 邬金鸣,侯跃芳,崔雷. 基于医学主题词标引规则的词共现聚类分析结果自动判读和表达的研究[J]. 数据分析与知识发现, 2020, 4(9): 133-144.
[9] 温萍梅,叶志炜,丁文健,刘颖,徐健. 命名实体消歧研究进展综述*[J]. 数据分析与知识发现, 2020, 4(9): 15-25.
[10] 席运江, 杜蝶蝶, 廖晓, 仉学红. 基于超网络的企业微博用户聚类研究及特征分析*[J]. 数据分析与知识发现, 2020, 4(8): 107-118.
[11] 杨旭,钱晓东. 基于改进的Vicsek模型的社会网络同步聚类算法*[J]. 数据分析与知识发现, 2020, 4(4): 119-128.
[12] 熊回香,李晓敏,李跃艳. 基于图书评论属性挖掘的群组推荐研究*[J]. 数据分析与知识发现, 2020, 4(2/3): 214-222.
[13] 魏家泽,董诚,何彦青,刘志辉,彭柯芸. 基于均衡段落和分话题向量的新闻热点话题检测研究*[J]. 数据分析与知识发现, 2020, 4(10): 70-79.
[14] 孙海霞,邓盼盼,李姣,沈柳,钱庆. 面向多源词表整合的概念自动更新策略研究*[J]. 数据分析与知识发现, 2020, 4(1): 121-130.
[15] 赵华茗,余丽,周强. 基于均值漂移算法的文本聚类数目优化研究 *[J]. 数据分析与知识发现, 2019, 3(9): 27-35.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn