|
|
|
| Research on Scientific and Technological Interaction Patterns Based on Topic Relevance Analysis |
| Liu Ziqiang1,2, Xu Haiyun1,3, Luo Rui1,2, Dong Kun4, Zhu Lijun3 |
1.Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041
2.Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049
3.Institute of Scientific and Technical Information of China, Beijing 100038
4.Institute of Scientific & Technical Information, Shandong University of Technology, Zibo 255200 |
|
|
|
|
Abstract Analyzing the internal mechanism of science and technology interaction at the micro level and revealing the modes of science and technology interaction quantitatively, automatically, and visually are of great significance to remedying the deficiency in current research on the internal relationship between science and technology and to revealing the development law and evolution characteristics of the collaborative innovation between science and technology. First, by constructing a co-occurrence matrix of multi-relationship fusion, research topics in papers and patents are identified on the basis of a community detection algorithm. Then, the structured data on science and technology topic association are constructed by synthesizing co-words, authors, and citation association degrees. Finally, the visualization map of science and technology topic evolution is drawn using the visualization method of topic evolution to assist in the analysis of science and technology interaction patterns. Empirical research was carried out with papers and patent data in the field of genetic engineering vaccines. The results showed that the main scientific and technological interaction modes in the field of genetic engineering vaccine were the S pattern, the T pattern, the S-T pattern, and the T-S pattern. Among them, the S pattern and the T pattern increased synergistically with the passage of time; the T-S pattern and the S-T pattern increased cross-wise with the passage of time.
|
|
Received: 23 May 2019
|
|
|
|
1 NarinF, HamiltonK S, OlivastroD. Linkage between agency-supported research and patented industrial technology[J]. Research Evaluation, 1995, 5(3): 183-187.
2 RipA. Science and technology as dancing partners[M]//Technological Development and Science in the Industrial Age. Dordrecht: Springer, 1992, 144: 231-270.
3 许海云, 武华维, 罗瑞, 等. 基于多元关系融合的科技文本主题识别方法研究[J]. 中国图书馆学报, 2019, 45(1): 82-94.
4 许海云, 董坤, 隗玲, 等. 科学计量中多源数据融合方法研究述评[J]. 情报学报, 2018, 37(3): 318-328.
5 董坤, 许海云, 罗瑞, 等. 科学与技术的关系分析研究综述[J]. 情报学报, 2018, 37(6): 642-652.
6 白如江, 冷伏海. k-clique社区知识创新演化方法研究[J]. 图书情报工作, 2013, 57(17): 86-94.
7 KimY G, SuhJ H, ParkS C. Visualization of patent analysis for emerging technology[J]. Expert Systems with Applications, 2008, 34(3): 1804-1812.
8 YanE J. Research dynamics, impact, and dissemination: A topic-level analysis[J]. Journal of the Association for Information Science and Technology, 2015, 66(11): 2357-2372.
9 WallaceM L, GingrasY, DuhonR. A new approach for detecting scientific specialties from raw cocitation networks[J]. Journal of the American Society for Information Science and Technology, 2009, 60(2): 240-246.
10 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.
11 BarbosaS D J, SilveiraM S, GaspariniI. What publications metadata tell us about the evolution of a scientific community: The case of the Brazilian human-computer interaction conference series[J]. Scientometrics, 2017, 110(1): 275-300.
12 NarinF, NomaE. Is technology becoming science?[J]. Scientometrics, 1985, 7(3-6): 369-381.
13 SchmochU. Tracing the knowledge transfer from science to technology as reflected in patent indicators[J]. Scientometrics, 1993, 26(1): 193-211.
14 Gl?nzelW, MeyerM. Patents cited in the scientific literature: An exploratory study of ‘reverse’ citation relations[J]. Scientometrics, 2003, 58(2): 415-428.
15 MeyerM, DebackereK, Gl?nzelW. Can applied science be ‘good science’? Exploring the relationship between patent citations and citation impact in nanoscience[J]. Scientometrics, 2010, 85(2): 527-539.
16 GaoJ P, DingK, TengL, et al. Hybrid documents co-citation analysis: Making sense of the interaction between science and technology in technology diffusion[J]. Scientometrics, 2012, 93(2): 459-471.
17 HuangM H, YangH W, ChenD Z. Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents[J]. Journal of Informetrics, 2015, 9(2): 237-249.
18 BassecoulardE, ZittM. Patents and publications[M]// Handbook of Quantitative Science and Technology Research. Dordrecht: Springer, 2004: 665-694.
19 赖院根. 期刊论文与专利文献的链接研究[J]. 图书情报知识, 2011(1): 63-69.
20 WangG B, GuanJ C. Measuring science-technology interactions using patent citations and author-inventor links: An exploration analysis from Chinese nanotechnology[J]. Journal of Nanoparticle Research, 2011, 13(12): 6245-6262.
21 张俊. 科学与技术的关联性研究[D]. 南京: 南京农业大学, 2014.
22 GardnerP. Representations of the relationship between science and technology in the curriculum[J]. Studies in Science Education, 1994, 24(1): 1-28.
23 VriesM D. The history of industrial research laboratories as a resource for teaching about science-technology relationships[J]. Research in Science Education, 2001, 31(1): 15-28.
24 李醒民. 科学和技术异同论[J]. 自然辩证法通讯, 2007, 29(1): 1-9, 110.
25 眭纪刚. 科学与技术: 关系演进与政策涵义[J]. 科学学研究, 2009, 27(6): 801-807.
26 刘小玲, 谭宗颖, 张超星. 国内外“科学-技术关系”研究方法述评——聚焦文献计量方法[J]. 图书情报工作, 2015, 59(13): 142-148.
27 GuanJ C, HeY. Patent-bibliometric analysis on the Chinese science—technology linkages[J]. Scientometrics, 2007, 72(3): 403-425.
28 吴曼. 基于科学计量的知识生产与技术开发时间关联分析[D]. 北京: 中国科学技术信息研究所, 2013.
29 KwonS, PorterA, YoutieJ. Navigating the innovation trajectories of technology by combining specialization score analyses for publications and patents: Graphene and nano-enabled drug delivery[J]. Scientometrics, 2016, 106(3): 1057-1071.
30 NoyonsE C M, RaanA F J V. Monitoring scientific developments from a dynamic perspective: Self-organized structuring to map neural network research[J]. Journal of the American Society for Information Science, 1998, 49(1): 68-81.
31 洛埃特·雷迭斯多夫. 科学计量学的挑战: 科学交流的发展、测度和自组织[M]. 北京: 科学技术文献出版社, 2003.
32 LeydesdorffL, CozzensS, Van den BesselaarP. Tracking areas of strategic importance using scientometric journal mappings[J]. Research Policy, 1994, 23(2): 217-229.
33 LeydesdorffL. Statistics for the dynamic analysis of scientometric data: the evolution of the sciences in terms of trajectories and regimes[J]. Scientometrics, 2013, 96(3): 731-741.
34 靖继鹏, 马费成, 张向. 情报科学理论[M]. 北京: 科学出版社, 2009.
35 刘则渊. 跨越学术分水岭[M]. 北京: 人民出版社, 2012.
36 PopperK R, WeissG. The logic of scientific discovery[J]. Physics Today, 1959, 12(11): 53-54.
37 万昊. 科学知识规模增长模式研究——基于数学建模和仿真论证[D]. 北京: 中国科学院大学, 2017.
38 布莱恩·阿瑟. 技术的本质[M]. 杭州: 浙江人民出版社, 2014.
39 BushV. As we may think[J]. Atlantic Monthly, 1979, 176(4): 36-44.
40 戴吾三. 技术创新简史[M]. 北京: 清华大学出版社, 2016.
41 BastianM, HeymannS, JacomyM. Gephi: An open source software for exploring and manipulating networks[C]// Proceedings of the Third International Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2009. |
|
|
|
