基于文献内聚度的非相关文献知识发现排序方法研究*

doi:10.11925/infotech.1003-3513.2009.06.10

现代图书情报技术

2009, Vol. 25

Issue (6): 50-54 https://doi.org/10.11925/infotech.1003-3513.2009.06.10

情报分析与研究

本期目录 | 过刊浏览 | 高级检索

基于文献内聚度的非相关文献知识发现排序方法研究*

张云秋¹冷伏海²

¹（吉林大学公共卫生学院长春 130021）
²（中国科学院国家科学图书馆北京 100190）

Research on the Literature Cohension-Based Ranking Method of Disjoint Literature-Based Discovery

Zhang Yunqiu¹ Leng Fuhai²

¹（School of Public Health, Jilin University, Changchun 130021,China)
²（National Science Library, Chinese Academy of Sciences, Beijing 100190,China)

摘要
参考文献
相关文章
Metrics

全文: PDF (511 KB)
输出: BibTeX | EndNote (RIS)

摘要

在对现有非相关文献知识发现中间集排序方法进行分析的基础上，以共现理论为基础，以主题关联度为着眼点，提出基于文献内聚度加权的B排序方法。并以Swanson的早期发现之一为基础，考察经文献内聚度加权和逆文献频率加权两种方法排序筛选后B的范围以及目标关联词和目标关联对的出现情况，以此作为评价其对B影响的依据。结果表明基于文献内聚度加权法能显著提高B的质量，从而提高发现效率。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张云秋
	冷伏海

关键词 ：非相关文献知识发现, 中间集, 文献内聚度, MeSH

Abstract：

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 words： Disjoint literature-based discovery B collection Literature cohesion MeSH

收稿日期: 2009-04-22 出版日期: 2009-06-25

G353.1

基金资助:

*本文系教育部社科研究基金规划项目“非相关文献知识发现的理论、方法及应用的拓展研究” （项目编号：07JA870005）的研究成果之一。

通讯作者: 张云秋 E-mail: zhangyq@mail.las.ac.cn

作者简介: 张云秋,冷伏海

引用本文:

张云秋,冷伏海. 基于文献内聚度的非相关文献知识发现排序方法研究*[J]. 现代图书情报技术, 2009, 25(6): 50-54.
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.

链接本文:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2009.06.10 或 https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2009/V25/I6/50

［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］.http://arrowsmith.psych.uic.edu/arrowsmith_uic/index.html.
［13］ MEDLINE Stopwords［EB/OL］. ［2008-12-31］.http://kiwi.uchicago.edu/stopwords_pubmed.

[1]	范雪雪, 王志荣, 徐晤, 梁银, 马小虎. 基于医学本体的术语相似度算法研究[J]. 现代图书情报技术, 2015, 31(12): 57-64.
[2]	张晗,崔雷,姜洋 . 运用非相关文献知识发现方法挖掘科研机构潜在的合作方向[J]. 现代图书情报技术, 2006, 1(4): 45-48.

Viewed

Full text

Abstract

Cited

Shared

Discussed