非相关文献知识发现研究进展被引量：2

Summary of New Progress in Research on Disjoint Literature Knowledge Discovery

导出

摘要本文通过大量的文献调研,对近5年来国内外知识发现研究的进展进行了系统的梳理,从知识发现研究过程中涉及的关联挖掘算法、中间词/目标词排序方法、过滤方法几个角度进行了方法总结,最后提出了未来知识发现的改进和发展方向。 By a comprehensive survey of literatures,this paper reviews the progress in research on knowledge discovery systematically both at home and abroad in recent 5 years.The paper summarizes the methods involved with knowledge discovery research,including association mining algorithm,between-term/target-term ranking method and filtering method.Finally,the paper points out the direction for the improvement and development of the future knowledge discovery.

作者杨渊高柳滨

机构地区中国科学院文献情报中心中国科学院上海药物研究所

出处《情报理论与实践》 CSSCI 北大核心 2011年第4期125-128,共4页 Information Studies:Theory & Application

关键词非相关文献知识发现综述 disjoint literature knowledge discovery summary

分类号 G350 [文化科学—情报学]

引文网络
相关文献

参考文献17

1SWANSON D R. Undiscovered public knowledge [J]. Library Quarterly, 1986, 56 (2) : 103-118.
2http://arrowsmith, psych, uic. edu/arrowsmith_ uic/index, ht- nil.
3HRISTOVSKI D, et al. Supporting discovery in medicine by association rule mining of bibliographic database [ J ]. Lecture Notes In Computer Science, 2000 (1910) : 44-6-451.
4AGRAWAL R, IMIELINSKI T, SWAMI A. Mining associations between sets of items in massive databases [ C ] //Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data, Washington, DC, 1993: 207-216.
5LINDSAY R K, GORDON M D. Literature based discovery by texical statistics [ J ]. Journal of American Society Information Science, 1999 (49): 674-685.
6SRINIVASAN P. Generating hypotheses from MEDLINE [ J]. Journal of American Society Information Science, 2004 (55) : 396-413.
7SALTON G. Automatic information organization and retrieval [M]. New York: Mc Graw-Hill, 1968.
8YETISGEN-YILDIZ M, PRATT W. Using statistical and knowledge-based approaches for literature based discovery [ J]. J Biomed Inform, 2006 (39): 600-611.
9WREN J D. Extending the mutual information measure to rank inferred literature relationship [ J]. BMC Bioinformatics, 2004 (5) : 145.
10HUANG W, et al. Mining scientific literature to predict new relationships [ J ]. Intelligent Data Analysis, 2005, 9 ( 2 ) : 219 -234.

二级参考文献33

1安新颖,冷伏海.基于非相关文献的知识发现原理研究[J].情报学报,2006,25(1):87-93. 被引量：36
2崔雷.专题文献高频主题词的共词聚类分析[J].情报理论与实践,1996,19(4):49-51. 被引量：149
3蒋颖.1995～2004年文献计量学研究的共词分析[J].情报学报,2006,25(4):504-512. 被引量：90
4陈文海,葛玮,郝克刚,侯红.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. 被引量：3
5Swanson D R. Undiscovered Public Knowledge[J]. Library Quarterly, 1986, 56(2) :103 -118.
6Gordon 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.
7Stegmann J, Grohmann G. Hypothesis Generation Guided by Co - word Clustering[J]. Scientometrics, 2003, 56(1 ):111 - 135.
8Van 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.
9Hristovski 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.
10Friedman 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.