Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (10): 2-12    DOI: 10.11925/infotech.1003-3513.2015.10.02
Current Issue | Archive | Adv Search |
Automatic Quality Evaluation of Social Tags
Zhang Chengzhi1,2, Li Lei1
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
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] It's important to improve application performance of social tags by selecting or recommending tags with high quality automatically. [Methods] The existing research on quality evaluation of social tags are separated into content and social attributes of tags, which don't combine these two attributes to evaluate the social tags. In this paper, the authors use tag's content and social attributes to evaluate the quality of tags by statistical machine learning model. [Results] Exprimental results show that with combining content and social attributes of tags, the quality evaluaton model based on SVM outperforms other models. [Limitations] Only use the blog tag data to evaluate the quality of social tags. The performance based on the social attributes are not perfect. Some social attributes can not effectively improve the automatic classification of social tags' quality. [Conclusions] This work is useful for improving the performance of the tags organization and related application.

Received: 21 July 2015      Published: 06 April 2016
:  G350  

Cite this article:

Zhang Chengzhi, Li Lei. Automatic Quality Evaluation of Social Tags. New Technology of Library and Information Service, 2015, 31(10): 2-12.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.10.02     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I10/2

[1] Trivedi A, Rai P, Daumé H, et al. Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Webpage Clustering [J]. ACM Transactions on Intelligent Systems and Technology, 2012, 3(4): Article No. 67.
[2] Zubiaga A, Martinez R, Fresno V. Getting the Most out of Social Annotations for Web Page Classification [C]. In: Proceedings of the 9th ACM Symposium on Document Engineering (DocEng2009), Munich, Germany. 2009: 74-83.
[3] Zhou D, Bian J, Zheng S, et al. Exploring Social Annotations for Information Retrieval [C]. In: Proceedings of the 17th International Conference on World Wide Web, Beijing, China. 2008: 715-724.
[4] Zhao S W, Du N, Nauerz A, et al. Improved Recommendation Based on Collaborative Tagging Behaviors [C]. In: Proceedings of the 13th International Conference on Intelligent User Interfaces (IUI'08), Canary Islands, Spain. 2008: 413-416.
[5] Lee S E, Han S S. Qtag: Introducing the Qualitative Tagging System [C]. In: Proceedings of the 18th Conference on Hypertext and Hypermedia (HT'07), Manchester, United Kingdom. 2007: 35-36.
[6] Sen S, Harper F M, LaPitz A, et al. The Quest for Quality Tags [C]. In: Proceedings of the 2007 International ACM Conference on Supporting Group Work (GROUP'07). 2007: 361-370.
[7] Van Damme C, Hepp M, Coenen T. Quality Metrics for Tags of Broad Folksonomies [C]. In: Proceedings of International Conference on Semantic Systems (I-SEMANTICS'08), Graz, Austria.2008: 118-125.
[8] Zhang S, Farooq U, Carroll J M. Enhancing Information Scent: Identifying and Recommending Quality Tags [C]. In: Proceedings of the ACM 2009 International Conference on Supporting Group Work (GROUP'09), Sanibel Island, USA. 2009: 1-10.
[9] Belém F M, Martins E F, Almeida J M, et al. Exploiting Co-occurrence and Information Quality Metrics to Recommend Tags in Web 2.0 Applications [C]. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM2010), Toronto, Canada. 2010: 1793-1796.
[10] 孙珂. 大规模文档标签自动标注技术研究[D]. 哈尔滨:哈尔滨工业大学, 2011. (Sun Ke. Research on Large-scale Document Automatic Tagging Technologies [D]. Harbin: Harbin Institute of Technology, 2011.)
[11] Guy M, Tonkin E. Folksonomies: Tidying up Tags? [J]. D-Lib Magazine, 2006, 12(1). http://www.dlib.org/dlib/january06/guy/01guy.html.
[12] Wu D, He D, Qiu J, et al. Comparing Social Tags with Subject Headings on Annotating Books: A Study Comparing the Information Science Domain in English and Chinese [J]. Journal of Information Science, 2013, 39(2): 169-187.
[13] Lee D H, Schleyer T. Social Tagging is no Substitute for Controlled Indexing: A Comparison of Medical Subject Headings and CiteULike Tags Assigned to 231, 388 Papers [J]. Journal of the American Society for Information Science and Technology, 2012, 63(9): 1747-1757.
[14] Hall C, Zarro M. What do You Call It?: A Comparison of Library-created and User-created Tags [C]. In: Procee­dings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL'11), Ottawa, Canada.2011: 53-56.
[15] Chen S J. User Tagging for Digital Archives: The Case of Commercial Keywords from the Grand Secretariat[C]. In: Proceedings of the International Conference on Asia-Pacific Digital Libraries (ICADL 2011), Beijing, China. 2011: 158-167.
[16] Syn S Y, Spring M B. Tags as Keywords - Comparison of the Relative Quality of Tags and Keywords [J]. Proceedings of the American Society for Information Science and Technology, 2009, 46(1):1-19.
[17] Lai V, Rajashekar C, Rand W. Comparing Social Tags to Microblogs [C]. In: Proceedings of 2011 IEEE 3rd International Conference on Privacy, Security, Risk and Trust and 2011 IEEE 3rd International Conference on Social Computing, Boston, USA. 2011: 1380-1383.
[18] Noh T G, Lee J K, Park S B, et al. Tag Quality Feedback: A Framework for Quantitative and Qualitative Feedback on Tags of Social Web [C]. In: Proceedings of the 11th Pacific Rim International Conference on Artificial Intelligence (PRICAI'10), Daegu, Korea. 2010: 637-642.
[19] Yi K, Yoo C Y. An Empirical Examination of the Associations Between Social Tags and Web Queries [J]. Information Research, 2012, 17(3). http://InformationR.net/ir/17-3/paper527. html
[20] Krestel R, Chen L. The Art of Tagging: Measuring the Quality of Tags [C]. In: Proceedings of the 3rd Asian Semantic Web Conference (ASWC'08), Bangkok, Thailand. 2008: 257-271.
[21] Gu X, Wang X, Li R, et al. Measuring Social Tag Confidence: Is It a Good or Bad Tag? [C]. In: Proceedings of the 12th International Conference on Web-Age Information Management (WAIM2011), Wuhan, China.2011: 94-105.
[22] 李蕾, 章成志. 社会化标签质量评估研究综述[J]. 现代图书情报技术, 2013(11): 22-29. (Li Lei, Zhang Chengzhi. Survey on Quality Measurement of Social Tags [J]. New Technology of Library and Information Service, 2013(11): 22-29.)
[23] 李蕾, 王冕, 章成志. 区分标签类型的社会化标签质量测评研究[J]. 图书情报工作, 2013, 57(23): 11-16. (Li Lei, Wang Mian, Zhang Chengzhi. Quality Evaluation of Social Tagging Based on the Type of Tags [J]. Library and Information Service, 2013,57(23): 11-16.)
[24] Li L, Zhang C Z. Quality Evaluation of Social Tags According to Web Resource Types [C]. In: Proceedings of the 23rd International Conference on World Wide Web (WWW'14 Companion), Seoul, Korea.2014: 1123-1128.
[25] Jones K S. A Statistical Interpretation of Term Specificity and Its Application in Retrieval [J]. Journal of Documentation, 1972, 28: 11-21.
[26] Kageura K, Umino B. Methods of Automatic Term Recogni­tion: A Review [J]. Terminology, 1996, 3(2): 259-289.
[27] 章成志. 多语言领域本体学习研究[M]. 南京: 南京大学出版社, 2012. (Zhang Chengzhi. Multilingual Domain Ontology Learning [M]. Nanjing: Nanjing University Press, 2012.)
[28] Wu X, Kumar V, Quinlan J R, et al. Top 10 Algorithms in Data Mining [J]. Knowledge and Information Systems, 2008, 14(1): 1-37.
[29] Vapnik V N. Statistical Learning Theory [M]. New York: Wiley, 1998.
[30] 魏宗舒, 等. 概率论与数理统计教程[M]. 北京: 高等教育出版社, 2010. (Wei Zongshu, et al. Textbook of Probability Theory and Mathematical Statistics [M]. Beijing: Higher Education Press, 2010.)

[1] Li Xiao, Qu Jiansheng. Review of Application and Evolution of Meta-Analysis in Social Sciences[J]. 数据分析与知识发现, 2021, 5(11): 1-12.
[2] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[3] Chen Shiji, Qiu Junping, Yu Bo. Topic Analysis of LIS Big Data Research with Overlay Mapping[J]. 数据分析与知识发现, 2021, 5(10): 51-59.
[4] Zheng Xinman, Dong Yu. Constructing Degree Lexicon for STI Policy Texts[J]. 数据分析与知识发现, 2021, 5(10): 81-93.
[5] Wang Yan, Wang Huyan, Yu Bengong. Chinese Text Classification with Feature Fusion[J]. 数据分析与知识发现, 2021, 5(10): 1-14.
[6] Fan Shaoping,Zhao Yuxuan,An Xinying,Wu Qingqiang. Classification Model for Medical Entity Relations with Convolutional Neural Network[J]. 数据分析与知识发现, 2021, 5(9): 75-84.
[7] Xu Liangchen, Guo Chonghui. Predicting Survival Rates for Gastric Cancer Based on Ensemble Learning[J]. 数据分析与知识发现, 2021, 5(8): 86-99.
[8] Xu Zengxulin, Xie Jing, Yu Qianqian. Designing New Evaluation Model for Talents[J]. 数据分析与知识发现, 2021, 5(8): 122-131.
[9] Zhang Jiandong, Chen Shiji, Xu Xiaoting, Zuo Wenge. Extracting PDF Tables Based on Word Vectors[J]. 数据分析与知识发现, 2021, 5(8): 34-44.
[10] Zhu Hou,Fang Qingyan. Quantifying and Examining Privacy Paradox of Social Media Users[J]. 数据分析与知识发现, 2021, 5(7): 111-125.
[11] Xie Hao,Mao Jin,Li Gang. Sentiment Classification of Image-Text Information with Multi-Layer Semantic Fusion[J]. 数据分析与知识发现, 2021, 5(6): 103-114.
[12] Yue Mingliang,Li Fushan,Tang Hongbo,Lv Xinhua,Ma Tingcan. Evaluating Consistency of Scholarly Article Reviewers[J]. 数据分析与知识发现, 2021, 5(4): 115-122.
[13] Zhang Xin,Wen Yi,Xu Haiyun. A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration[J]. 数据分析与知识发现, 2021, 5(3): 88-100.
[14] Zhang Jinzhu, Yu Wenqian. Topic Recognition and Key-Phrase Extraction with Phrase Representation Learning[J]. 数据分析与知识发现, 2021, 5(2): 50-60.
[15] Li Danyang, Gan Mingxin. Music Recommendation Method Based on Multi-Source Information Fusion[J]. 数据分析与知识发现, 2021, 5(2): 94-105.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn