学科领域研究前沿探测方法研究述评

doi:10.3772/j.issn.1000-0135.2019.08.011

情报学报

2019, Vol. 38

Issue (8): 872-880 DOI: 10.3772/j.issn.1000-0135.2019.08.011

研究进展与文献综述

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学科领域研究前沿探测方法研究述评

黄晓斌¹, 吴高^1,2

1.中山大学资讯管理学院，广州 510006
2.广西师范大学图书馆，桂林 541004

Research Review of Frontier Detection Methods in Subject Fields

Huang Xiaobin¹, Wu Gao^1,2

1.School of Information Management, Sun Yat-sen University, Guangzhou 510006
2.Library of Guangxi Normal University, Guilin 541004

摘要
图/表
参考文献
相关文章 (13)

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摘要首先介绍了研究前沿的相关概念，然后将学科领域研究前沿探测方法划分为基于引用关系的探测方法（共被引分析法、文献耦合分析法和直接引用分析法）、基于文本内容的探测方法（词频分析法、共词分析法和主题模型法）和基于复合关系的探测方法三种类型，并对每种类型探测方法的发展历程、应用案例以及效果优劣进行评述。最后总结了不同类型研究前沿探测方法的缺陷，并预测了其未来发展趋势和方向。

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	黄晓斌
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关键词 ：学科领域, 科学研究, 研究前沿, 研究热点, 探测方法

收稿日期: 2018-10-28

基金资助:广东省哲学社会科学优势重点学科建设项目“学科前沿与研究热点监测方法研究”（GDXK201704）。

作者简介: 黄晓斌，男，1961年生，博士，教授，博士生导师，研究方向为竞争情报研究、信息分析、网络信息开发利用、数字化信息资源管理等，E-mail:isshxb@mail.sysu.edu.cn

引用本文:

黄晓斌, 吴高. 学科领域研究前沿探测方法研究述评[J]. 情报学报, 2019, 38(8): 872-880.
Huang Xiaobin, Wu Gao. Research Review of Frontier Detection Methods in Subject Fields. 情报学报, 2019, 38(8): 872-880.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2019.08.011 或 https://qbxb.istic.ac.cn/CN/Y2019/V38/I8/872

1 EvansH. Emerging technologies IPC has Inaugural meeting[OL]. http://www.whitehouse.gov/blog/2010/05/15/emerging-technologies-ipc-has-unaugural-meeting.
2 Canada Foundation for Innovation. Innovation made in Canada best in the world (2009-10 Annual Report)[OL]. http://www.innovation.ca/docs/annualreport/CFIAnnualReport2009-2010.pdf.
3 马楠, 官建成. 利用引文分析方法识别研究前沿的进展与展望[J]. 中国科技论坛, 2006(4): 110-113, 128.
4 KontostathisA, GalitskyL M, PottengerW M, et al. A survey of emerging trend detection in textual data mining[M]// Survey of Text Mining. New York: Springer, 2004: 185-224.
5 钱力. 信息可视化领域研究热点及演化特征的可视化分析[J]. 情报杂志, 2013(6): 114-120.
6 de Solla PriceD J. Networks of scientific papers[J]. Science, 1965, 149(3683): 510-515.
7 GarfieldE, SmallH. Identifying the changing frontiers of science[EB/OL]. [2019-05-01]. http://www.garfield.library.upenn.edu/ papers/362/362.html.
8 UphamS P, SmallH. Emerging research fronts in science and technology: Patterns of new knowledge development[J]. Scientometrics, 2010, 83(1): 15-38.
9 SmallH. Co-citation in the scientific literature: A new measure of the relationship between two documents[J]. Journal of the American Society for Information Science, 1973, 24(4): 265-269.
10 GarfieldE. Research fronts[EB/OL]. [2019-05-01]. http://www.garfield.library.upenn.edu/essays94.html.
11 PerssonO. The intellectual base and research fronts of JASIS 1986-1990[J]. Journal of the American Society for Information Science, 1994, 45(1): 31-38.
12 MorrisS A, YenG, WuZ, et al. Time line visualization of research fronts[J]. Journal of the Association for Information Science and Technology, 2003, 54(5): 413-422.
13 KesslerM M. Bibliographic coupling between scientific papers[J]. American Documentation, 1963,14(1): 10-25.
14 BraamR R, MoedH F, van RaanA F J. Mapping of science by combined co-citation and word analysis.Ⅰ. Structural aspects[J]. Journal of the American Society for Information Science and Technology, 1991, 42(4): 233-251.
15 ChenC M. CiteSpaceⅡ: Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for information Science and Technology, 2006, 57(3): 359-377.
16 陈超美. CiteSpaceⅡ: 科学文献中新趋势与新动态的识别与可视化[J]. 陈悦, 侯剑华, 梁永霞, 译. 情报学报, 2009, 28(3): 401-421.
17 ArisA, ShneidermanB, QazvinianV, et al. Visual overviews for discovering key papers and influences across research fronts[J]. Journal of the American Society for Information Science and Technology, 2009, 60(11): 2219-2228.
18 冯佳, 张云秋. 科学前沿探测方法述评[J]. 图书馆杂志, 2017, 36(5): 29-35.
19 赵继. 全面认识和合理使用ESI(基本科学指标)[J]. 中国高等教育, 2015(1): 31-34.
20 韩涛. 知识结构演化深度分析的方法及其实现[D]. 北京: 中国科学院研究生院, 2008.
21 WeinbergB H. Bibliographic coupling: A review[J]. Information Storage and Retrieval, 1974, 10(5-6): 189-196.
22 VladutzG, CookJ. Bibliographic coupling and subject relateness[J]. Proceedings of the American Society for Information Science, 1984, 21: 204-207.
23 Gl?nzelW, CzerwonH J. A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level[J]. Scientometrics, 1996, 37(2): 195-221.
24 SchiebelE. Visualization of research fronts and knowledge bases by three-dimensional areal densities of bibliographically-coupled publications and co-citations[J]. Scientometrics, 2012, 91(2): 557-566.
25 宋艳辉, 武夷山. 基于作者文献耦合分析的情报学知识结构研究[J]. 图书情报工作, 2014, 58(1): 117-123.
26 LiuJ S, LuL Y Y, LuW M. Research fronts in data envelopment analysis[J]. Omega, 2016, 58: 33-45.
27 ZhangL, Gl?nzelW, YeF Y. The dynamic evolution of core documents: An experimental study based on h-related literature (2005-2013)[J]. Scientometrics, 2016, 106(1): 369-381.
28 GarfieldE. Historiographic mapping of knowledge domains literature[J]. Journal of Information Science, 2004, 30(2): 119-145.
29 KlavansR, BoyackK W. Identifying a better measure of relatedness for mapping science[J]. Journal of the American Society for Information Science and Technology, 2006, 57(2): 251-263.
30 ShibataN, KajikawaY, TakedaY, et al. Detecting emerging research fronts based on topological measures in citation networks of scientific publications[J]. Technovation, 2008, 28(11): 758-775.
31 ShibataN, KajikawaY, TakedaY, et al. Comparative study on methods of detecting research fronts using different types of citation[J]. Journal of the American Society for Information Science and Technology, 2009, 60(3): 571-580.
32 葛菲, 谭宗颖. 学科领域主题新兴趋势探测方法研究——基于关键词生命周期和引文分析[J]. 情报理论与实践, 2013, 36(9): 78-82.
33 马费成, 张勤. 国内外知识管理研究热点——基于词频的统计分析[J]. 情报学报, 2006, 25(2): 163-171.
34 巩永强, 刘莉. 基于词频分析法的情报学研究热点透析[J]. 图书馆学研究, 2011(13): 9-13.
35 储节旺, 钱倩. 基于词频分析的近10年知识管理的研究热点及研究方法[J]. 情报科学, 2014, 32(10): 156-160.
36 刘奕杉, 王玉琳, 李明鑫. 词频分析法中高频词阈值界定方法适用性的实证分析[J]. 数字图书馆论坛, 2017(9): 42-49.
37 KleinbergJ. Bursty and hierarchical structure in streams[C]// Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2002: 91-101.
38 ManeK K, BornerK. Mapping topics and topic bursts in PNAS[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(Supplement 1): 5287-5290.
39 洪娜, 张智雄, 乐小虬. 基于决策树的潜在爆发词探测方法[J]. 情报学报, 2012, 31(3): 228-241.
40 张英杰. 科技领域前沿计量探测方法研究[D]. 北京: 中国科学院研究生院, 2011.
41 王梦婷. 基于突变检测的主题突变分析研究[J]. 情报科学, 2016, 34(12): 36-39.
42 郑乐丹. 基于突变检测的学科领域新兴研究趋势探测分析[J]. 情报杂志, 2012, 31(9): 50-53.
43 Le MarcM, CourtialJ P, SenkovskaE D, et al. The dynamics of research in the psychology of work from 1973 to 1987: From the study of companies to the study of professions[J]. Scientometrics, 1991, 21(1): 69-86.
44 RipA, CourtialJ P. Co-word maps of biotechnology: An example of cognitive scientometrics[J]. Scientometrics, 1984, 6(6): 381-400.
45 CozzensS E, CallonM, LawJ, et al. Mapping the dynamics of science and technology: Sociology of science in the real world[M]. Contemporary Sociology, 1988, 17(6): 815.
46 PetersH P F, van RaanA F J. Co-word-based science maps of chemical engineering. PartⅠ: Representations by direct multidimensional scaling[J]. Research Policy, 1993, 22(1): 23-45.
47 SuH N, LeeP C. Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in technology foresight[J]. Scientometrics, 2010, 85(1): 65-79.
48 郝伟霞, 滕立, 陈悦, 等. 基于共词分析的中国能源材料领域主题研究[J]. 情报杂志, 2011, 30(6): 70-75.
49 李纲, 李轶. 一种基于关键词加权的共词分析方法[J]. 情报科学, 2011, 29(3): 321-324, 332.
50 杨彦荣, 张阳. 加权共词分析法研究[J]. 情报理论与实践, 2011, 34(4): 61-63.
51 程齐凯, 王晓光. 一种基于共词网络社区的科研主题演化分析框架[J]. 图书情报工作, 2013, 57(8): 91-96.
52 魏瑞斌. 基于内容分析的国内图书情报学研究方法创新研究——以共词分析方法为例[J].图书情报工作, 2016, 60(24): 107-114.
53 许晓阳, 郑彦宁, 刘志辉. 论文和专利相结合的研究前沿识别方法研究[J]. 图书情报工作, 2016, 60(24): 97-106.
54 DeerwesterS, DumaisS T, FurnasG W, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41(6): 391-407.
55 HofmannT. Probabilistic latent semantic indexing[C]// Proceedings of the 22nd Annual International ACM SIGIR Conference on Pesearch and Development in Information Retrieval. New York: ACM Press, 1999: 50-57.
56 BleiD M, NgA Y, JordanM I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
57 ZhangJ, GhahramaniZ, YangY M. A probabilistic model for online document clustering with application to novelty detection[C]// Proceedings of the 17th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2004: 1617-1624.
58 BleiD M, LaffertyJ D. Dynamic topic model[C]// Proceedings of the 23rd International Conference on Machine Learning. New York: ACM Press, 2006: 113-120.
59 WuQ Q, ZhangC D, AnX Y, et al. Topic segmentation model based on ATNLDA and co-occurrence theory and its application in stem cell field[J]. Journal of Information Science, 2013, 39(3): 319-332.
60 范云满, 马建霞. 基于LDA与新兴主题特征分析的新兴主题探测研究[J]. 情报学报, 2014, 33(7): 698-711.
61 LeeY S, LoR, ChenC Y, et al. News topics categorization using latent Dirichlet allocation and Sparse representation classifier[C]// Proceedings of the IEEE International Conference on Consumer Electronics. New York: IEEE, 2015: 136-137.
62 白如江, 冷伏海, 廖君华. 一种基于多数据源主题对比的科学研究前沿识别方法[J]. 情报理论与实践, 2017, 40(8): 43-48, 36.
63 BraamR R, MoedH F, van RaanA F J. Mapping of science by combined co-citation and word analysis. I. Structural aspects[J]. Journal of the American Society for Information Science, 1991, 42(4): 233-251.
64 GlenissonP, Gl?nzelW, JanssensF, et al. Combining full text and bibliometric information in mapping scientific disciplines[J]. Information Processing & Management, 2005, 41(6): 1548-1572.
65 van den BesselaarP, HeimeriksG. Mapping research topics using word-reference co-occurrence: A method and an exploratory case study[J]. Scientometrics, 2006, 68(3): 377-393.
66 侯海燕, 刘则渊, 栾春娟. 基于知识图谱的国际科学计量学研究前沿计量分析[J]. 科研管理, 2009, 30(1): 164-170.
67 BoyackK W, KlavansR. Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?[J]. Journal of the American Society for Information Science and Technology, 2010, 61(12): 2389-2404.
68 周丽英, 冷伏海, 左文革. 引文耦合增强的共词分析方法改进研究——以ESI农业科学研究主题划分为例[J]. 情报理论与实践, 2015, 38(11): 120-125.
69 XieP. Study of international anticancer research trends via co-word and document co-citation visualization analysis[J]. Scientometrics, 2015, 105(1): 611-622.