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:
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:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.08.12     OR     https://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] Huang Mingxuan,Jiang Caoqing,Lu Shoudong. Expanding Queries Based on Word Embedding and Expansion Terms[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
[2] Xu Guang,Ren Ming,Song Chengyu. Extracting China’s Economic Image from Western News[J]. 数据分析与知识发现, 2021, 5(5): 30-40.
[3] Dai Bing,Hu Zhengyin. Review of Studies on Literature-Based Discovery[J]. 数据分析与知识发现, 2021, 5(4): 1-12.
[4] Yu Chuanming, Wang Manyi, Lin Hongjun, Zhu Xingyu, Huang Tingting, An Lu. A Comparative Study of Word Representation Models Based on Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 28-40.
[5] Xia Tian. Extracting Key-phrases from Chinese Scholarly Papers[J]. 数据分析与知识发现, 2020, 4(7): 76-86.
[6] Shi Hongbo,Guo Hongmei,Yue Ting,Qian Li,Huang Dingyu,Chang Zhijun. Developing Modularity Scientometrics System with Distributed Technology[J]. 数据分析与知识发现, 2020, 4(2/3): 231-238.
[7] Du Jian. Measuring Uncertainty of Medical Knowledge: A Literature Review[J]. 数据分析与知识发现, 2020, 4(10): 14-27.
[8] Peng Guan,Yuefen Wang. Advances in Patent Network[J]. 数据分析与知识发现, 2020, 4(1): 26-39.
[9] Mingxuan Huang,Shoudong Lu,Hui Xu. Cross-Language Information Retrieval Based on Weighted Association Patterns and Rule Consequent Expansion[J]. 数据分析与知识发现, 2019, 3(9): 77-87.
[10] 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.
[11] 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.
[12] 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.
[13] Jiao Yan,Jing Ma,Kang Fang. Computing Text Semantic Similarity with Syntactic Network of Co-occurrence Distance[J]. 数据分析与知识发现, 2019, 3(12): 93-100.
[14] Zhang Ning,Yin Lemin,He Lifeng. Impacts of “Poster-Follower” Sentiment on Stock Market Performance[J]. 数据分析与知识发现, 2018, 2(6): 1-12.
[15] Fan Xinyue,Cui Lei. 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.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn