科学前沿探测方法述评

摘要/Abstract

摘要：

文章在系统梳理科学前沿相关概念的基础上，依据科学前沿探测方法的原理不同，评述了基于引文的、基于社团结构的、基于文本内容的混合方法，其中主题分析法是科学前沿探测研究的新趋势。文章重点探讨了科学前沿的探测过程，提出4个关键环节，并针对各个环节对比分析了不同方法的优缺点，以期为科学前沿的探测研究提供支持和帮助。

关键词:

科学前沿, 方法述评, 引文分析, 共词分析, 主题模型

Abstract:

After analyzing the concept of “scientific fronts”, the paper reviews the detecting methods that are different from each other in terms of principles. It singles out thematic analysis as the new trend,and compares the four different methods for their advantages and disadvantages in the four key steps in detecting scientific fronts.

Key words:

Scientific fronts, Method review, Citation analysis, Co-word analysis, Topic model

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.

Feng Jia, Zhang Yunqiu. A Review of Detecting Methods for Scientific Fronts[J]. Libraly Journal, 2017, 36(5): 29-35.

参考文献

[ 1 ] Price D. Networks of Scientific Papers[J]. Science,1965, 149(3683): 510-515.

[ 2 ] Garfield E, Small H. Identifying the changing frontiers of science[J]. Evolution, 1989, 1(5165): 11.

[3] Upham S P, Small H. Emerging Research Fronts in Science and Technology: Patterns of New Knowledge Development[J]. Scientometrics, 2010, 83(1): 15-38.

[ 4 ] Small H. 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.

[ 5 ] Garfield E. Research Fronts[J]. Current Contents,1994, 41(10): 3-7.

[ 6 ] Persson O. The Intellectual Base and Research Fronts of JASIS 1986-1990[J]. Journal of the American Society for information Science, 1994,45(1): 31-38.

[ 7 ] Morris S. Time Line Visualization of Research Fronts[J]. Journal of the American Society for Information Science and Technology, 2003, 54(5):413-422.

[ 8 ] Braam R R, et al. 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.

[ 9 ] Bhattacharya S, Basu P. Mapping a Research Area at the Micro Level Using Co-word Analysis[J].Scientometrics, 1998, 43(3): 359-360, 371-372.

[10] Chen C M. CiteSpaceⅡ: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature[J]. Journal of American Society for information Science and Technology, 2006, 57(3):359-377.

[11] 陈超美, 陈悦, 侯剑华. CiteSpaceⅡ: 科学文献中新趋势与新动态的识别与可视化[J]. 情报学报,2009, 28(3): 401-421.

[12] 陈仕吉. 科学研究前沿探测方法综述[J]. 现代图书情报技术, 2009(9): 28-33.

[13] 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, 32(6): 114-120.

[14] Lee J Y. Identifying the Research Fronts in Korean Library and Information Science by Document Cocitation Analysis[J]. Journal of the Korean Society for information Management, 2015, 32(4): 77-106.

[15] Liu J S, Lu L Y, Lu W M. Research fronts in data envelopment analysis[J]. Omega, 2016(58): 33-45.

[16] Zhang L, Gl.nzel W, Fred Y Y. The Dynamic evolution of core documents: an experimental study based on h-related literature(2005-2013)[J].Scientometrics, 2016, 106(1): 369-381.

[17] Shibata N, Kajikawa Y, Takeda Y, et al. Detecting emerging research fronts based on topological measures in citation networks of scientific publications[J]. Technovation, 2008, 28(11): 758-775

[18] Shibata N, Kajikawa Y, Takeda Y, 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.

[19] Shibata N, Kajikawa Y, Sakata I. Detecting Potential Technological Fronts by Comparing Scientific Papers and Patents[J]. Foresight, 2011, 13(5): 51-60.

[20] Klavans R, Boyack K 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.

[21] Wang N, Liang H, Jia Y, et al. Cloud computing research in the IS discipline: A citation/ co-citation analysis[J]. Decision Support Systems, 2016(86):35-47.

[22] Huang M, Chang C P. A comparative study on detecting research fronts in the organic lightemitting diode(OLED)field using bibliographic coupling and co-citation[J]. Scientometrics, 2015,102(3): 2041-2057.

[23] Girvan M, Newman M E J. Community Structure in Social and Biological Networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(12): 7821-7826.

[24] 李晓佳, 张鹏, 狄增如. 复杂网络中的社团结构[J].复杂系统与复杂性科学, 2008, 5(3): 19-42.

[25] Betteneourt L M A, Kaiser D, Kaur J. Scientific discovery and topological transitions in collaboration networks[J]. Journal of Informetrics, 2009, 3(3): 210-221.

[26] Prabhakaran T, Lathabai H H, Changat M. Detection of paradigm shifts and emerging fields using scientific network: A case study of Information Technology for Engineering[J]. Technological Forecasting and Social Change, 2015(91): 124-145.

[27] Shi F, Foster J G, Evans J A. Weaving the fabric of science: Dynamic network models of science’s unfolding structure[J]. Social Networks, 2015(43):73-85.

[28] Takeda Y, Kajikawa Y. Optics: A Bibliometric Approach to Detect Emerging Research Domains and Intellectual Bases[J]. Scientometrics, 2009,78(3): 543-558.

[29] Fan Y, Li M, Zhang P, et al. The Effect of Weight on Community Structure of Networks[J]. Physica A, 2007, 378(2): 583-590.

[30] Blondel V D, Guillaume J L, Lambiotte R, et al.Fast unfolding of communities in large networks [J].Journal of statistical mechanics: theory and experiment, 2008(10): 10008.

[31] Wallace M L, Gingras Y, Duhon R. A New Approach for Detecting Scientific Specialties[J]. Journal of the American Society for Information Science and Technology, 2009, 60(2): 241-246.

[32] Granovetter M. Strength of weak ties[J]. American Journal of soeiology, 1973(8): 1360-1380.

[33] Takeda Y, Kajikawa Y. Optics: A Bibliometric Approach to Detect Emerging Research Domains and Intellectual Bases[J]. Scientometrics, 2009,78(3): 543-558.

[34] 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(10):156-160.

[35]秦琴琴, 乜勇. 基于词频分析和可视化共词网络图的国内创客研究热点分析[J]. 现代教育技术,2016(1): 113-119.

[36] Kleinberg J. Bursty and hierarchical structure in streams[J]. Data Mining and Knowledge Discovery,2003, 7(4): 373-397.

[37] 洪娜, 张智雄, 乐小虬, 等. 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.

[38] 张英杰, 冷伏海. 基于离群点的前沿趋势探测方法研究[J]. 高技术通讯, 2011, 21(11): 1109-1114.

[39] 王莉亚. 基于离群数据的主题演化研究[D]. 北京:中国科学院研究生院, 2012.

[40] Su H N, Lee P C. Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight[J]. Scientometrics, 2010,85(1): 65-79.

[41] 郝韦霞, 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, 6(30):70-75.

[42] Han Y, Wang T. Hotspot Domains and Knowledge Base in the Research of Special Education Teachers in Recent Years—A Bibliometric Analysis of CSSCI Publications between 1998 and 2013[J]. Journal ofTeacher Education, 2015(1): 7.

[43] He Q, Wang G. Hotspots Evolution and Frontier Analysis of Lean Construction Research-Integrated Scientometric Analysis using the Web of Science and Scopus Databases[J]. Frontiers of Engineering Management, 2015, 2(2): 141-147.

[44] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[J]. the Journal of machine Learning research, 2003(3): 993-1022.

[45] 曾利, 李自力, 谭跃进. 基于动态LDA的科研文献主题演化分析[J]. 软件, 2014, 35(5): 102-107.

[46] 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.

[47] Wu Q Q, Zhang C D, An X Y. 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.

[48] Lee Y S, Lo R, Chen C Y, et al. News topics categorization using latent Dirichlet allocation and sparse representation classifier[C]//Consumer Electronics-Taiwan(ICCE-TW), 2015 IEEE International Conference on. IEEE, 2015: 136-137.

[49] 叶春蕾, 冷伏海. 基于共词分析的学科主题演化方法改进研究[J]. 情报理论与实践, 2012, 35(3):79-82.

[50] 叶春蕾, 冷伏海. 基于概率模型的主题识别方法实证研究[J]. 情报科学, 2013(2): 135-142.

[51] Glenisson P, Glänzel W, Janssens F, et al. Combining full text and bibliometric information in mapping scientific disciplines[J]. Information Processing & Management, 2005, 41(6): 1548-1572.

[52] Xie P. Study of international anticancer research trends via co-word and document co-citation visualization analysis[J]. Scientometrics, 2015,105(1): 611-622.

[53] 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(11): 120-125.[54] Klavans R, Boyack K W. 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, 2010, 61(12): 2389-2404.

[55] Janssens F. Clustering of scientific fields by integrating text mining and bibliometrics[R].Doctoral dissertation. Faculty of Engineering,Katholieke Universiteit Leuven, Belgium. 2007.

[1]	刘志伟吕斌（上海大学图书情报档案系）摘. 引用认同视角下信息计量跨学科研究的现况、评价与发展策略——以Web of Science数据为例#br#[J]. 图书馆杂志, 2021, 40(9): 97-111.
[2]	石泽顺肖明. 基于本体和关联数据的全文引文分析方法研究 [J]. 图书馆杂志, 2021, 40(4): 100-108.
[3]	刘艳华华薇娜谭华军刘婧. 21世纪以来青少年阅读的国际研究主题演化分析[J]. 图书馆杂志, 2020, 39(9): 30-37.
[4]	卢文辉李战. 零被引与高被引图书馆学硕士学位论文引文特征的比较分析[J]. 图书馆杂志, 2020, 39(1): 76-84.
[5]	石泽顺肖明. 基于本体和关联数据的引文分析方法研究[J]. 图书馆杂志, 2019, 38(12): 82-91.
[6]	周耀林柴昊赵跃. 国际图情领域大数据研究现状与趋势探析[J]. 图书馆杂志, 2019, 38(12): 16-27.
[7]	李锋. 兼顾中低频关键词的共词分析实践 ——以图书情报学高被引文章为例[J]. 图书馆杂志, 2018, 37(4): 34-42.
[8]	张品慧张瑜婷赵星. 多维h分布：广义h测度及其应用探索[J]. 图书馆杂志, 2018, 37(4): 18-26.
[9]	郝群周小康徐琳张立彬. 复旦大学图书馆数学类英文图书引进及保障数据集[J]. 图书馆杂志, 2018, 37(12): 83-86.
[10]	阮光册夏磊. 基于共现分析的文本主题词聚类研究[J]. 图书馆杂志, 2018, 37(11): 99-104.
[11]	冯佳张云秋. 基于语义距离的共词方法改进研究[J]. 图书馆杂志, 2017, 36(7): 66-73.
[12]	郑淑芬, 朱玉奴, 缪家鼎. 高校图书馆视角下的专利服务工作思考——基于文献计量和可视化分析[J]. 图书馆杂志, 2015, 34(2): 53-57.
[13]	汪东伟, 李梅, 殷沈琴, 张计龙. 基于动态数据的经济学领域研究热点分析[J]. 图书馆杂志, 2014, 33(12): 24-31.
[14]	王铁梅,吴志荣. 学术图书核心出版社测定方法比较研究——以法律类图书出版社为例[J]. 图书馆杂志, 2013, 32(6): 30-35.
[15]	朱惠灵. 馆藏质量评价方法之比较——以心理学类学术图书为例[J]. 图书馆杂志, 2013, 32(4): 61-64.