Please wait a minute...
New Technology of Library and Information Service  2007, Vol. 2 Issue (9): 62-65    DOI: 10.11925/infotech.1003-3513.2007.09.13
Current Issue | Archive | Adv Search |
Automated Folksonomy Research of Tag Resource Based on Synergetic Mechanism
Wu Pengfei1    Meng Xiangzeng2    Ma Fengjuan Lu Wenpeng3
1 (Library of Shijiazhuang College, Shijiazhuang 050035,China)
2 (School of Communication, Shandong Normal University, Jinan 250014,China)
3 (School of Information Science and Technology, Shandong Institute
of Light Industry, Jinan 250100, China)
Export: BibTeX | EndNote (RIS)      

This paper, which based on the structure and revelatory rules to Web page’s segmentation and the area semantic identification, realizes the understanding of Web page’s semantics, and presents the definition of the Web multimedia relevant text. Besides that, combining with its distribution characteristics, it has adopted three levels analysis method to carry on the extraction, including the individual level, the area level and the page level, thus realized the Web multimedia relevant text accurately to extract.

Key wordsSemantic      Web multimedia relevant text      Segmentation      Extraction      Mapping     
Received: 08 January 2007      Published: 25 September 2008


Corresponding Authors: Wu Pengfei     E-mail:
About author:: Wu Pengfei,Meng Xiangzeng,Ma Fengjuan,Lu Wenpeng

Cite this article:

Wu Pengfei,Meng Xiangzeng,Ma Fengjuan,Lu Wenpeng. Automated Folksonomy Research of Tag Resource Based on Synergetic Mechanism. New Technology of Library and Information Service, 2007, 2(9): 62-65.

URL:     OR

[1] Eakins J P.Automatic Image Content Retrieval-Are We Getting Anywhere[R]?In:Proc of 3rd Int’l Confon Electronic Library and Visual Information Research.De Montfort University,Milton Keynes:Aslib,1996:123-135.
[2] Gudivada V N ,Raghavan V V .Content-based Image Retrieval System[J].IEEE Computer,1995,28(9):18-22.
[3] Chang S,Smith J,Beigi M,et al.Visual Information Rieval From Large Distributed Onliner Epositories[J].Communictions of ACM,1997(40):63-71.
[4] Rowe N,Frew B.Automatic Caption Localization for Photographs on World-Wide Web pages[J].Information Process and Management,1998(34):95-107.
[5] Frankel C,Swain M,Athitsos V.WebSeer:An Image Search Engine for the World-Wide Web[R].Technical Report 94-14.Computer Science Department,University of Chicago,August,1996.
[6] Harmandas V,Sanderson M,Dunlop M.Image Retrieval by Hpertext Links[C].In:Proceedings of the ACM SIGIR’97 Conference on Research and Development in Information Retrieval.Philadelphia,PA,July,1997,296-33.
[7] 万钧,钟亦平,傅维明,等.启发式相关文本提取技术研究[J].小型微型计算机系统,2004,25(4):582-586.
[8] 吴鹏飞,孟祥增,刘俊晓,等.基于结构与内容的网页主题信息提取研究[J].山东大学学报(理学版),2006,41(3):131-134.
[9] Zhiguo Gong,Leong Hou U,Chan Wa Cheang.Web Image Indexing by Using Associated Texts[J]. Knowledge and Information Systems,  2006, 10(2): 243-264.
[10] 林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务[M].北京:清华大学出版社,2003:200-201.

[1] Wang Yifan,Li Bo,Shi Hua,Miao Wei,Jiang Bin. Annotation Method for Extracting Entity Relationship from Ancient Chinese Works[J]. 数据分析与知识发现, 2021, 5(9): 63-74.
[2] Ma Jiangwei, Lv Xueqiang, You Xindong, Xiao Gang, Han Junmei. Extracting Relationship Among Military Domains with BERT and Relation Position Features[J]. 数据分析与知识发现, 2021, 5(8): 1-12.
[3] Han Hui, Liu Xiuwen. Automatic Scoring for Subjective Questions in Maritime Competency Assessment[J]. 数据分析与知识发现, 2021, 5(8): 113-121.
[4] Chai Qingfeng, Shi Linyan, Mei Shan, Xiong Haitao, He Huixin. Extracting Knowledge Elements of Sci-Tech Literature Based on Artificial and Machine Features[J]. 数据分析与知识发现, 2021, 5(8): 132-144.
[5] Tan Ying, Tang Yifei. Extracting Citation Contents with Coreference Resolution[J]. 数据分析与知识发现, 2021, 5(8): 25-33.
[6] 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.
[7] Li Wenna, Zhang Zhixiong. Entity Alignment Method for Different Knowledge Repositories with Joint Semantic Representation[J]. 数据分析与知识发现, 2021, 5(7): 1-9.
[8] Yu Xuehan, He Lin, Xu Jian. Extracting Events from Ancient Books Based on RoBERTa-CRF[J]. 数据分析与知识发现, 2021, 5(7): 26-35.
[9] Zhao Danning,Mu Dongmei,Bai Sen. Automatically Extracting Structural Elements of Sci-Tech Literature Abstracts Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(7): 70-80.
[10] Chen Xingyue, Ni Liping, Ni Zhiwei. Extracting Financial Events with ELECTRA and Part-of-Speech[J]. 数据分析与知识发现, 2021, 5(7): 36-47.
[11] Xu Zheng,Le Xiaoqiu. Generating AND-OR Logical Expressions for Semantic Features of Categorical Documents[J]. 数据分析与知识发现, 2021, 5(5): 95-103.
[12] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[13] Yan Qiang,Zhang Xiaoyan,Zhou Simin. Extracting Keywords Based on Sememe Similarity[J]. 数据分析与知识发现, 2021, 5(4): 80-89.
[14] Shi Xiang,Liu Ping. Extraction and Representation of Domain Knowledge with Semantic Description Model and Knowledge Elements——Case Study of Information Retrieval[J]. 数据分析与知识发现, 2021, 5(4): 123-133.
[15] Hu Shaohu,Zhang Yingyi,Zhang Chengzhi. Review of Keyword Extraction Studies[J]. 数据分析与知识发现, 2021, 5(3): 45-59.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938