PDF(696 KB)
战略性新兴产业多领域知识融合路径研究——基于引用网络和文本信息的分析
刘宇飞, 苗仲桢, 黎凌峰, 孔德婧
中国工程科学 ›› 2020, Vol. 22 ›› Issue (2) : 120-129.
PDF(696 KB)
PDF(696 KB)
战略性新兴产业多领域知识融合路径研究——基于引用网络和文本信息的分析
Multi-domain Knowledge Convergence Trajectory Analysis of Strategic Emerging Industries Based on Citation Network and Text Information
针对战略性新兴产业开展技术融合过程分析，有助于深入理解产业技术的产生过程和发展规律，从而捕捉领域发展动向、推动产业健康发展。本文针对战略性新兴产业中呈现融合发展趋势且备受社会关注的高端装备制造、新一代信息技术、新医药、新能源4个技术领域进行多案例研究，以期识别出技术融合发展的路径和程度。采用基于引用网络和文本信息的知识融合路径分析方法，使用图神经网络同时将论文的引用网络、标题和摘要信息编码为向量；分析4个技术领域的论文数据，识别出了 5 条技术融合路径。研究结果表明，信息技术与数控设备技术、生物医药与太阳能光伏技术均呈现深度融合的趋势，且前者的融合程度更为深入；数控设备与太阳能光伏技术、信息技术与太阳能光伏技术也呈现融合趋势，但限于发展时间较短而显融合程度较浅；数控设备与生物医药技术领域尚未呈现融合发展的趋势。
The analysis of technology convergence process for strategic emerging industries is helpful to deeply understand the generation process and development law of industrial technology, thereby helping master the development trend of the field and promoting the healthy development of the industry. To identify the trajectory and degree of technology convergence of the strategic emerging industries, this study conducts a multi-case study on four fields which present a trend of convergence and attract social attention, namely, high-end equipment manufacturing, new-generation information technology, new medicine, and new energy. This study adopts a knowledge convergence trajectory analysis method based on citation network and text information. It utilizes a graph neural network model and encodes the citation network, title, and abstract of the publications as vectors. Five knowledge convergence trajectories are identified, after analyzing the data of the selected four technical fields. The research results show that information technology and numerical control equipment, biomedicine and solar photovoltaic technology have shown a trend of deep convergence, respectively; and the convergence of the information technology and numerical control equipment is deeper. Numerical control equipment and solar photovoltaic technology, information technology and solar photovoltaic technology have shown a converging trend, respectively; however, the current degree of convergence is still insufficient, due to the late start of convergence. Numerical control equipment and biomedicine have not shown any trend of convergence.
新兴产业 / 知识融合 / 图神经网络 / 引用网络 / 主题模型
emerging industries / knowledge convergence / graph neural networks / citation network / topic model
| [1] |
刘宇飞, 周源, 廖岭. 大数据分析方法在战略性新兴产业技术预 见中的应用 [J]. 中国工程科学, 2016, 18(4): 121–128. Liu Y F, Zhou Y, Liao L. Application of big data analysis method in technology foresight for strategic emerging industries [J]. Strategic Study of CAE, 2016, 18(14): 121–128.
|
| [2] |
Lee S, Lee S, Seol H, et al. Using patent information for designing new product and technology: Keyword based technology roadmapping [J]. R & D Management, 2008, 38(2): 169–188.
|
| [3] |
Kim E, Cho Y, Kim W. Dynamic patterns of technological convergence in printed electronics technologies: Patent citation network [J]. Scientometrics, 2014, 98(2): 975–998.
|
| [4] |
Hacklin F, Marxt C, Fahrni F. Coevolutionary cycles of convergence: An extrapolation from the ICT industry [J]. Technological Forecasting and Social Change, 2009, 76(6): 723– 736.
|
| [5] |
Curran C S, Broring S, Leker J. Anticipating converging industries using publicly available data [J]. Technological Forecasting and Social Change, 2010, 77(3): 385–395.
|
| [6] |
Hacklin F. Management of convergence in innovation [M]. Heidelberg: Physica-Verlag HD, 2008.
|
| [7] |
Cho Y, Kim E, Kim W. Strategy transformation under technological convergence: Evidence from the printed electronics industry [J]. International Journal of Technology Management , 2015, 67(2–4): 106–131.
|
| [8] |
周源, 董放, 刘宇飞. 融合新兴领域知识融合过程研究——以生 物信息领域为例 [J]. 图书情报工作, 2019, 63(8): 127–134. Zhou Y, Dong F, Liu Y F. Research on the process of knowledge fusion in emerging fields: A case study of bioinformatics [J]. Library and Information Service, 2019, 63(8): 127–134.
|
| [9] |
Zhou Y, Dong F, Kong D J, et al. Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies [J]. Technological Forecasting and Social Change, 2019, 144: 205–220.
|
| [10] |
Song B, Suh Y. Identifying convergence fields and technologies for industrial safety: LDA-based network analysis [J]. Technological Forecasting and Social Change, 2019, 138: 115–126.
|
| [11] |
Lee K. Patterns and processes of contemporary technology fusion: The case of intelligent robots [J]. Asian Journal of Technology Innovation, 2007, 15(2): 45–65.
|
| [12] |
Dosi G. Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change [J]. Research Policy, 1993, 22(2): 147–162.
|
| [13] |
Curran C S, Leker J. Patent indicators for monitoring convergence: Examples from NFF and ICT [J]. Technological Forecasting and Social Change, 2011, 78(2): 256–273.
|
| [14] |
Kose T, Sakata I. Identifying technology convergence in the field of robotics research [J]. Technological Forecasting and Social Change, 2019, 146: 751–766.
|
| [15] |
李丫丫, 赵玉林. 基于专利的技术融合分析方法及其应用 [J]. 科学学研究, 2016, 34 (2): 203–211. Li Y Y, Zhao Y L. Method and application of technological convergence analysis base on patents [J]. Studies in Science of Science, 2016, 34(2): 203–211.
|
| [16] |
苗红, 宋昱晓, 黄鲁成. 基于知识流动网络评价的技术融合趋势 研究 [J]. 科技进步与对策, 2018, 35(6): 131–139. Miao H, Song Y X, Huang L C. Research on technology convergence trend based on evaluation of the knowledge flow network [J]. Science & Technology Progress and Policy, 2018, 35(6): 131–139.
|
| [17] |
No H J, Park Y. Trajectory patterns of technology fusion: Trend analysis and taxonomical grouping in nanobiotechnology [J]. Technological Forecasting and Social Change, 2010, 77(1): 63–75.
|
| [18] |
邱均平, 杨强. 技术融合的计量分析——以4种新兴信息技术为 例 [J]. 图书情报工作, 2014 (14): 90–97. Qiu J P, Yang Q. Informetrics analysis of technological fusion: Taking four emerging information technologies for example [J]. Library and Information Service, 2014 (14): 90–97.
|
| [19] |
Kim N, Lee H, Kim W, et al. Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data [J]. Research Policy, 2015, 44(9): 1734–1748.
|
| [20] |
Kim D H, Lee H, Kwak J. Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network [J]. Research Policy, 2017, 46(7): 1234–1254.
|
| [21] |
Zhou J, Cui G, Zhang Z, et al. Graph neural networks: A review of methods and applications [EB/OL]. (2018-12-20) [2019-12-05]. https://arxiv.org/abs/1812.08434.
|
| [22] |
Wu Z, Pan S, Chen F, et al. A comprehensive survey on graph neural networks [EB/OL]. (2019-01-03) [2019-12-05]. https://arxiv.org/abs/1901.00596.
|
| [23] |
Battaglia P W, Hamrick J B, Bapst V, et al. Relational inductive biases, deep learning, and graph networks [EB/OL]. (2018-06-04) [2019-12-05]. https://arxiv.org/abs/1806.01261.
|
| [24] |
Pan S, Hu R, Long G, et al. Adversarially regularized graph autoencoder for graph embedding [C]// Proceedings of the twentyseventh international joint conference on artificial intelligence. Stockholm, Sweden: IJCAI, 2019: 2609–2615.
|
| [25] |
Yu W, Zheng C, Cheng W, et al. Learning deep network representations with adversarially regularized autoencoders [C]// Guo Y, Farooq F. The 22nd ACM SIGKDD international conference on knowledge discovery and data mining. London, UK: ACM, 2018: 2663–2671.
|
| [26] |
Kipf T N, Welling M. Variational graph auto-encoders [EB/OL]. (2016-11-21) [2019-12-15]. https://arxiv.org/abs/1611.07308.
|
| [27] |
周爱武, 于亚飞. K-Means聚类算法的研究 [J]. 计算机技术与发 展, 2011, 21(2): 62–65. Zhou A W, Yu Y F. The research about clustering algorithm of K-Means [J]. Computer Technology and Development, 2011, 21(2): 62–65.
|
| [28] |
周源, 杜俊飞, 刘宇飞, 等. 基于引用网络和文本挖掘的技术演 化路径识别 [J]. 情报杂志, 2018, 37(10): 80–85. Zhou Y, Du J F, Liu Y F, et al. Identifying technology evolution pathways by integrating citation network and text mining [J]. Journal of Intelligence, 2018, 37(10): 80–85.
|
| [29] |
Blei D M, Ng A Y, Jordan M I, et al. Latent dirichlet allocation [J]. Journal of Machine Learning Research, 2003, 3: 993–1022.
|
| [30] |
Zhou Y, Zang J, Miao Z, et al. Upgrading pathways of intelligent manufacturing in China: Transitioning across technological paradigms [J]. Engineering, 2019, 5(4): 691–701.
|
/
| 〈 |
|
〉 |
