战略性新兴产业前沿技术探测研究——以量子计算领域为例

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2175 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the background of technological competition,identifying emerging frontier technologies is crucial for understanding the trajectory of technological innovation,informing strategic planning,and fostering industrial development and transformation.This study focuses on funds,papers and patents as analytical subjects.Firstly,the LDA topic model is employed to discern topics,subsequently constructing a topic co-occurrence network based on topic relationships.Then,according to the characteristics of emerging frontier technology,multi-dimensional evaluation metrics are formulated considering timing,informetrics,structure and evolution.A multi-source data correlation analysis framework is proposed.Finally,based on the comprehensive calculation results,topic type discrimination is undertaken to explore the development patterns of topics.Empirical research in the field of quantum computing reveals different types of topics,including potential emerging frontier technology,growth emerging frontier technology,robust emerging frontier technology,hot frontier technology and early topics.These insights provide reference and guidance for future frontier technology strategies.

Key words： Emerging frontier technology Strategic emerging industry Quantum computing Technology identification Co-occurrence network

Received: 19 June 2023

PACS:

F124.3

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liu Panpan
	Wang Li

Cite this article:

Liu Panpan,Wang Li. Research on Frontier Technology Identification in Strategic Emerging Industry ——A Case Study on Quantum Computing[J]. , 2024, 5(6): 46-57.

URL:

http://www.zgkjlt.org.cn/EN/ OR http://www.zgkjlt.org.cn/EN/Y2024/V5/I6/46

[1]新华社.中共中央关于制定国民经济和社会发展第十四个五年规划和二〇三五年远景目标的建议[EB/OL]. (2020-11-03)[2020-11-06].http://www.gov.cn/zhengce/2020-11/03/content_5556991.htm.
[2]颜学明,刘建明.基于专利计量的中美量子计算技术发展态势研究[J].科技管理研究,2022,42 (23):152-159.
[3]于杰平,王丽.中美量子计算研发现状对比分析及启示[J].世界科技研究与发展,2022,44 (1):35-45.
[4]任海英,李真.基于输入输出型SAO网络的核心技术链识别方法研究——以量子计算领域为例[J].图书情报工作,2021,65 (19):117-129.
[5]PRICE D.Networks of scientific papers[J].Science,1965,149 (3683):510-515.
[6]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.
[7]GARFIELD E.Research fronts[J].Current Contents,1994,41 (10):3-7.
[8]潘教峰,王海霞,冷伏海,等. 《2022研究前沿》——11个大学科领域发展趋势与重点研究问题[J].中国科学院院刊,2023,38 (1):154-166.
[9]廖鹏飞,李明鑫,万锋.基于长尾关键词的领域新兴前沿探寻模型构建研究[J].情报杂志,2020,39 (3):51-55.
[10]武川,王宏起,王珊珊.前沿技术识别与预测方法研究:基于专利主题相似网络与技术进化法则[J].中国科技论坛,2023 (4):34-42.
[11]刘琦岩,曾文,车尧.面向重点领域科技前沿识别的情报体系构建研究[J].情报学报,2020,39 (4):345-356.
[12]科技部.国家 “十二五”科学和技术发展规划[EB/OL]. (2011-07-14)[2023-08-24].https://www.gov.cn/govweb/jrzg/2011-07/13/content_1905911.htm.
[13]周萌,朱相丽.新兴技术概念辨析及其识别方法研究进展[J].情报理论与实践,2019,42 (10):162-169.
[14]高楠,周庆山.新兴技术概念辨析与识别方法研究进展[J].现代情报,2023,43 (4):150-164.
[15]窦永香,开庆,王佳敏.一种基于图表示学习的潜在颠覆性技术识别方法[J].情报学报,2023,42 (6):637-648.
[16]刘盼盼,王丽.关系网络视角下新兴技术识别研究进展[J].图书情报工作,2022,66 (11):139-150.
[17]YU D,YAN Z.Combining machine learning and main path analysis to identify research front:from the perspective of science-technology linkage[J].Scientometrics,2022,127 (7):4251-4274.
[18]王云飞,王志玲,宋伟,等.水下潜器全球研发前沿识别与国家研发布局[J].科技管理研究,2022,42 (14):14-23.
[19]孙明汉,郭梦园,朱秀珠.基于专利图谱的全球船舶领域创新前沿探测研究[J].中国科技论坛,2023 (3):73-81.
[20]MARRONE M.Application of entity linking to identify research fronts and trends[J].Scientometrics,2020,122 (1):357-379.
[21]宋凯,朱彦君.专利前沿技术主题识别及趋势预测方法——以人工智能领域为例[J].情报杂志,2021,40 (1):33-38.
[22]王菲菲,刘明.Altmetrics视角下的交叉学科研究前沿探测——以医学信息学领域为例[J].情报学报,2020,39 (10):1011-1020.
[23]白如江,刘博文,冷伏海.基于多维指标的未来新兴科学研究前沿识别研究[J].情报学报,2020,39 (7):747-760.
[24]BLEI D M,NG A Y,JORDAN M I.Latent dirichlet allocation[J].Journal of Machine Learning Research,2003,3 (4/5):993-1022.
[25]SIEVERT C,SHIRLEY K.LDAvis:a method for visualizing and interpreting topics[C]//Proceedings of the Workshop on Interactive Language Learning,Visualization,and Interfaces.Baltimore,Maryland,USA:Association for Computational Linguistics,2014.
[26]刘俊婉,龙志昕,王菲菲.基于LDA主题模型与链路预测的新兴主题关联机会发现研究[J].数据分析与知识发现,2019,3 (1):104-117.
[27]SWANSON D R.Fish oil,raynaud's syndrome,and undiscovered public knowledge[J].Perspectives in Biology and Medicine,1986,30 (1):7-18.
[28]SWANSON D R.Undiscovered public knowledge[J].The Library Quarterly,1986,56 (2):103-118.
[29]宋欣娜,郭颖,席笑文.基于专利文献的多指标新兴技术识别研究[J].情报杂志,2020,39 (6):76-81,88.
[30]郝雯柯,杨建林.基于语义表示和动态主题模型的社科领域新兴主题预测研究[J].情报理论与实践,2023,46 (2):184-193.
[31]黄璐,朱一鹤,张嶷.基于加权网络链路预测的新兴技术主题识别研究[J].情报学报,2019,38 (4):335-341.
[32]李昌,杨中楷,董坤.基于多维属性动态变化特征的新兴技术识别研究[J].情报学报,2022,41 (5):463-474.
[33]周海炜,吴成凤.基于专利SAO结构和多指标评价的新兴技术识别研究——以手机芯片领域为例[J].情报杂志,2022,41 (2):86-94,48.
[34]周云泽,闵超.基于LDA模型与共享语义空间的新兴技术识别——以自动驾驶汽车为例[J].数据分析与知识发现,2022,6 (Z1):55-66.
[35]TSOURI M,HANSEN T,HANSON J,et al.Knowledge recombination for emerging technological innovations:the case of green shipping[J].Technovation,2022,114:102454.
[36]WOO S,YOUTIE J,OTT I,et al.Understanding the long-term emergence of autonomous vehicles technologies[J].Technological Forecasting and Social Change,2021,170:120852.
[37]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.
[38]杨金庆,陆伟,吴乐艳.面向学科新兴主题探测的多源科技文献时滞计算及启示——以农业学科领域为例[J].情报学报,2021,40 (1):21-29.
[39]刘博文,白如江,周彦廷,等.基金项目数据和论文数据融合视角下科学研究前沿主题识别——以碳纳米管领域为例[J].数据分析与知识发现,2019,3 (8):114-122.
[40]王兴旺,董珏,余婷婷,等.基于多种类型信息计量分析的前沿技术预测方法研究[J].情报杂志,2018,37 (10):70-75,89.
[41]卢超,侯海燕,DING YING,等.国外新兴研究话题发现研究综述[J].情报学报,2019,38 (1):97-110.
[42]Office National Quantum Coordination.Quantum frontiers[R].United States of America:The White House,2020.
[43]FEDOROV A K,GELFAND M S.Towards practical applications in quantum computational biology[J].Nature Computational Science,2021,1 (2):114-119.
[44]王新文,金贤敏.光量子计算研究与应用[J].信息通信技术与政策,2022 (7):37-43.