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New Technology of Library and Information Service  2009, Vol. 25 Issue (6): 50-54    DOI: 10.11925/infotech.1003-3513.2009.06.10
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Research on the Literature Cohension-Based Ranking Method of Disjoint Literature-Based Discovery
Zhang Yunqiu1  Leng Fuhai
1(School of Public Health, Jilin University, Changchun 130021,China)  
2(National Science Library, Chinese Academy of Sciences, Beijing  100190,China)
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 Based on the analysis of the existing ranking methods of B collection of disjoint literature-based discovery, this paper proposes literature cohesion-based ranking method according to the co-occurrence theory and the subject relative degree. Then, an experiment is conducted comparing with one of Swanson’s former discoveries. The size of B and the occurrence of the target terms and target relations are explored to evaluate the effect on B of the two methods including the literature cohesion-based weight and the inverse document frequency-based weight. The results of the experiment indicate that the literature cohesion-based ranking method can improve the quality of B and enhance efficiency of discovery accordingly.

Key wordsDisjoint literature-based discovery      B collection      Literature cohesion      MeSH     
Received: 22 April 2009      Published: 25 June 2009


Corresponding Authors: yun-qiu zhang     E-mail:
About author:: Zhang Yunqiu,Leng Fuhai

Cite this article:

Zhang Yunqiu,Leng Fuhai. Research on the Literature Cohension-Based Ranking Method of Disjoint Literature-Based Discovery. New Technology of Library and Information Service, 2009, 25(6): 50-54.

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[1] Swanson D R. Undiscovered Public Knowledge[J]. Library Quarterly, 1986, 56(2):103-118.
[2] Gordon M D, Dumais S. Using Latent Semantic Indexing for Literature Based Discovery[J]. Journal of the American Society for Information Science and Technology, 1998, 49(8): 674-685.
[3] Stegmann J, Grohmann G. Hypothesis Generation Guided by Co-word Clustering[J]. Scientometrics, 2003, 56(1):111–135.
[4] Van der Eijk C C, Van Mulligen E M, Kors J A, et al. Constructing an Associative Concept Space for Literature-based Discovery[J]. Journal of the American Society for Information Science and Technology, 2004, 55(5): 436-444.
[5] Hristovski D, Peterlin B, Mitchell J A, et al. Using Literature-based Discovery to Identify Disease Candidate Genes[J]. International Journal of Medical Informatics, 2005, 74 (2-4) : 289-298.
[6] 赖茂生. 计算机情报检索[M]. 北京:北京大学出版社,2006.
[7] 张琪玉. 情报语言学基础[M]. 武汉: 武汉大学出版社, 1997.
[8] Friedman  P W, Winnick B L, Friedman C P, et al. Development of a MeSH-based Index of Faculty Research Interests[C]. In:Proceedings of Annual AMIA Symposium, 2000:265-269.
[9] Harris Z. Language and Information[M]. New York:Columbia University Press, 1987.
[10] 陈文海, 葛玮, 郝克刚,等. 面向对象软件中类内聚度度量分析与研究[J]. 计算机应用研究, 2007, 24(7): 40-42.
[11] Swanson D R. Migraine and Magnesium: Eleven Neglected Connections[J]. Perspectives in Biology and Medicine, 1988, 31(4): 526-557.
[12] Arrowsmith[EB/OL]. [2008-12-31].
[13] MEDLINE Stopwords[EB/OL]. [2008-12-31].

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