Please wait a minute...
New Technology of Library and Information Service  2008, Vol. 24 Issue (8): 70-75    DOI: 10.11925/infotech.1003-3513.2008.08.12
Current Issue | Archive | Adv Search |
Development of a Text Mining System Based on the Co-occurrence of Bibliographic Items in Literature Databases
Cui Lei   Liu Wei   Yan Lei   Zhang Han  Hou Yuefang  Huang Yingna  Zhang Hao
(Department of Information Management and Information System (Medicine), China Medical University, Shenyang  110001,China)
Download: PDF(999 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  

This paper presents a text mining system based on the co-occurrence of bibliographic items in literature databases. This system produces the principal bibliometric indicators of a given document set oriented to PubMed and Web of Science, and some of results are presented by visualization techniques. Further more, it provides cluster analysis and association analysis by investigating the co-occurrence data of high-frequent MeSH terms, high-productive authors, highly-cited papers and highly-cited authors. Using these approaches users can mining the potential association rules among MeSH terms, and engage scientometric investigations.

Key wordsText Mining      Co-occurrence      Bibliographic System      Scientometrics     
Received: 19 March 2008      Published: 25 August 2008
: 

G254

 
Corresponding Authors: Cui Lei     E-mail: lcui@mail.cmu.edu.cn
About author:: Cui Lei, Liu Wei,Yan Lei,Zhang Han,Hou Yuefang,Huang Yingna,Zhang Hao

Cite this article:

Cui Lei, Liu Wei,Yan Lei,Zhang Han,Hou Yuefang,Huang Yingna,Zhang Hao. Development of a Text Mining System Based on the Co-occurrence of Bibliographic Items in Literature Databases. New Technology of Library and Information Service, 2008, 24(8): 70-75.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.08.12     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2008/V24/I8/70

[1] Ding J, Berleant D.  MedKit:A Helper Toolkit for Automatic Mining of MEDLINE/PubMed Citations[J]. Bioinformatics, 2005,21(5):694-695.
[2] 张晗, 任志国, 张健, 等. 基于主题词关联规则的医学文本数据库数据挖掘的尝试[J]. 医学信息学杂志, 2008(1):32-35.
[3] 张晗, 王晓瑜, 崔雷. 共词分析法与文献被引次数结合研究专题领域的发展态势[J]. 情报理论与实践, 2007,30(3):378-380.
[4] 张晗, 刘鹏年, 崔雷. 国外消化性溃疡文献研究热点的分析[J]. 世界华人消化杂志,2007,15(10):1150-1155.
[5] 李范, 王飞, 侯跃芳, 等. 病案相关研究的文献计量学分析[J]. 中华医学图书情报杂志,2007,16 (3):70-72.

[1] Yanan Yang,Wenhui Zhao,Jian Zhang,Shen Tan,Beibei Zhang. Visualizing Policy Texts Based on Multi-View Collaboration[J]. 数据分析与知识发现, 2019, 3(6): 30-41.
[2] Jing Shi,Chenlu Li,Yuxing Qian,Liqin Zhou,Bin Zhang. Information Needs of Domestic and International HCQA Users ——An Empirical Analysis[J]. 数据分析与知识发现, 2019, 3(5): 1-10.
[3] Mengji Zhang,Wanyu Du,Nan Zheng. Predicting Stock Trends Based on News Events[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
[4] Ning Zhang,Lemin Yin,Lifeng He. Impacts of “Poster-Follower” Sentiment on Stock Market Performance[J]. 数据分析与知识发现, 2018, 2(6): 1-12.
[5] Xinyue Fan,Lei Cui. Using Text Mining to Discover Drug Side Effects: Case Study of PubMed[J]. 数据分析与知识发现, 2018, 2(3): 79-86.
[6] Meimei Chen, Kangjie Xue. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model[J]. 数据分析与知识发现, 2017, 1(3): 38-45.
[7] Qiangbing Wang,Chengzhi Zhang. Constructing Users Profiles with Content and Gesture Behaviors[J]. 数据分析与知识发现, 2017, 1(2): 80-86.
[8] Tong Liu,Jingcheng Yang. Evaluating Online Healthcare Consultation Feedbacks Based on Signal Transmission Algorithm[J]. 数据分析与知识发现, 2017, 1(11): 29-36.
[9] Xiufang Xie,Xiaolin Zhang. Integrated Analysis and Visualization of Sci-Tech Roadmaps: Case Study of Renewable Energy[J]. 数据分析与知识发现, 2017, 1(1): 16-25.
[10] Wang Yuefen,Jin Jialin. Characteristics and Development Trends of Papers from “New Technology of Library and Information Service”[J]. 现代图书情报技术, 2016, 32(9): 1-16.
[11] Yao Zhaoxu,Ma Jing. Extracting Topic and Opinion from Microblog Posts with New Algorithm[J]. 现代图书情报技术, 2016, 32(7-8): 78-86.
[12] Lan Qiujun,Liu Wenxing,Li Weikang,Hu Xingye. Sentiment Analysis of Financial Forum Textual Message[J]. 现代图书情报技术, 2016, 32(4): 64-71.
[13] Qiang Bi, Jian Liu, Yulai Bao. A New Text Clustering Method Based on Semantic Similarity[J]. 数据分析与知识发现, 2016, 32(12): 9-16.
[14] Lin Yuanyuan,Zhan Hongfei,Yu Junhe,Li Changjiang,Zhang Fan. Using Product Reviews to Analyze Sentiment Fluctuation of Consumer[J]. 现代图书情报技术, 2016, 32(11): 44-53.
[15] Zhao Dongxiao,Wang Xiaoyue,Bai Rujiang,Liu Ziqiang. Semantic Text Mining Methodologies for Intelligence Analysis[J]. 现代图书情报技术, 2016, 32(10): 13-24.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn