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Frontier Identification of Emerging Scientific Research Based on Multi-indicators |
Bai Rujiang1,2, Liu Bowen1, Leng Fuhai3 |
1.Institute of Scientific and Technical Information, Shandong University of Technology, Zibo 255049 2.National Library of China, Beijing 100081 3.Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190 |
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Abstract A new trend of scientific and technological revolution is surging in the world today, and identifying the frontier of emerging scientific research in the future will help the scientific layout of the areas of future competition. Based on the integration of scientific and technological planning text data, fund project data, and paper data, using natural language processing technology, text theme recognition technology, and complex network analysis technology, this study proposes a research frontier discriminating model using multi-dimensional indexes, such as topic intensity and topic novelty. Empirical research has been conducted in the field of carbon nanotube research. The results demonstrate that the model can effectively identify the hotspot research frontier, emerging research frontier, potential research frontier, and weak research frontier.
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Received: 09 May 2019
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1 de Solla Price D J. Networks of scientific papers[J]. Science, 1965, 149(3683): 510-515. 2 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. 3 Garfield E. Research fronts[J]. Current Contents, 1994, 41(10): 3-7. 4 中国科学院科技战略咨询研究院, 中国科学院文献情报中心, 科睿唯安. 2018研究前沿及分析解读[M]. 北京: 科学出版社, 2019. 5 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. 6 Morris S A, Yen G, Wu Z, et al. Time line visualization of research fronts[J]. Journal of the American Society for Information Science and Technology, 2003, 54(5): 413-422. 7 Garfield E. Historiographic mapping of knowledge domains literature[J]. Journal of Information Science, 2004, 30(2): 119-145. 8 Boyack K W, Klavans R. 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. 9 马海群, 吕红. 2000—2009年《情报科学》文献计量学分析与研究[J]. 情报科学, 2011, 29(6): 867-873. 10 Kleinberg J. Bursty and hierarchical structure in streams[C]// Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2002: 91-101. 11 Chen C M. CiteSpace II: 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. 12 张龙晖. 大数据时代的专利分析[J]. 信息系统工程, 2014(2): 148-149. 13 Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022. 14 Blei D M, Lafferty J D. Dynamic topic models[C]// Proceedings of the 23rd International Conference on Machine Learning. New York: ACM Press, 2006: 113-120. 15 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, 31(2): 135-139. 16 李广建, 化柏林. 大数据分析与情报分析关系辨析[J]. 中国图书馆学报, 2014, 40(5): 14-22. 17 周文杰, 张彤彤, 高冲. 共词分析预测研究前沿的表面效度研究: 基于自然语言处理[J]. 高校图书馆工作, 2018, 38(2): 17-21. 18 刘小平, 李泽霞. 基于共词分析的量息学前沿热点分析[J]. 科学观察, 2014, 9(5): 13-22. 19 许晓阳, 郑彦宁, 赵筱媛, 等. 研究前沿识别方法的研究进展[J]. 情报理论与实践, 2014, 37(6): 139-144. 20 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. 21 Thorndike R L. Who belongs in the family?[J]. Psychometrika, 1953, 18(4): 267-276. 22 Steyvers M, Griffiths T. Probabilistic topic models[M]// Handbook of Latent Semantic Analysis. London: Routledge, 2007: 424-440. 23 Joachims T. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization[C]// Proceedings of the Fourteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers, 1997: 143-151. 24 程应武, 杨志, 魏浩, 等. 碳纳米管气体传感器研究进展[J]. 物理化学学报, 2010, 26(12): 3127-3142. 25 Jabbari F, Rajabpour A, Saedodin S. Viscosity of carbon nanotube/water nanofluid[J]. Journal of Thermal Analysis and Calorimetry, 2019, 135(3): 1787-1796. 26 汪兵洋, 郑治文, 赵康, 等. 非共价键功能化石墨烯/碳纳米管负载型金属配合物催化剂及催化反应中的应用[J]. 分子催化, 2019, 33(1): 90-101. 27 Iijima S. Helical microtubules of graphitic carbon[J]. Nature, 1991, 354(6348): 56-58. |
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