情报研究

颠覆性技术产生过程的多源数据多特征项可视化研究

  • 庞弘燊 ,
  • 钟秀梅
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  • 深圳大学图书馆 深圳 518060
庞弘燊,副研究馆员,博士

收稿日期: 2022-03-25

  修回日期: 2022-07-19

  网络出版日期: 2022-10-25

基金资助

本文系国家社会科学基金一般项目"基于论文-专利交叉共现分析技术的基础-应用产业创新演化路径分析方法研究"(项目编号:19BTQ074)研究成果之一。

A Visualization Study for Understanding Disruptive Technology Evolution Based on Multi-Feature Co-Occurrence Network Analysis of Multi-Source Literature

  • Pang Hongshen ,
  • Zhong Xiumei
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  • Library of Shenzhen University, Shenzhen 518060

Received date: 2022-03-25

  Revised date: 2022-07-19

  Online published: 2022-10-25

摘要

[目的/意义] 颠覆性技术创新是最高阶的创新,基于多源科技文献挖掘对颠覆性技术产生过程进行可视化研究,能够更好地为各层各级的关键技术识别、预测和研发提供支撑。[方法/过程] 通过构建科学-技术相关多源科技文献所组成的多特征项共现分析方法体系模型,对多源科技文献中的多特征项关联关系进行系统分析;利用此理论分析方法,构建颠覆性技术产生过程中科学技术多特征项共现的理论研究方法体系,并建立颠覆性技术演化过程中关键技术节点因素的可视化模型。[结果/结论] 本研究为有效地监测和识别颠覆性技术产业创新的演化路径和绘制颠覆性技术的产业技术发展路线图提供研究基础,为颠覆性技术演化路径识别监测分析方法提供多数据来源、多维度数据分析、多层次知识发现的理论方法体系。

本文引用格式

庞弘燊 , 钟秀梅 . 颠覆性技术产生过程的多源数据多特征项可视化研究[J]. 图书情报工作, 2022 , 66(19) : 122 -131 . DOI: 10.13266/j.issn.0252-3116.2022.19.012

Abstract

[Purpose/Significance] Disruptive technological innovation is the highest level of innovation, and visualization study of the disruptive technology generation process can be implemented based on multi-source scientific and technological literature mining, which provides support for the identification, prediction and development of key technologies at all levels.[Method/Process] This paper constructed a multi-feature co-occurrence network analysis model of multi-source literatures, aimed at studying the correlation network of co-occurrence features which were exacted from the multi-source literatures related to science and technology. Based on the theoretical analysis model, a new research theory was established to explore the multi-feature co-occurrence network related to science and technology during the generation process of disruptive technology, and a visualization model was built to identify the key points in the evolution of disruptive technology.[Result/Conclusion] This study provides a research foundation for effectively monitoring and identifying the evolution path of disruptive technology innovation and drawing a road map for disruptive technology development, and also provides a new theoretical method system with multi data source, multi-dimension data analysis, and multi-level knowledge discovery for the identification and monitoring analysis method of the disruptive technology evolution path.

参考文献

[1] 杨卫. 从跟跑到领跑的历史性转身[EB/OL].[2022-06-04]. http://theory.people.com.cn/big5/n1/2017/0604/c40531-29316150.html.
[2] CHRISTENSEN C M, BOHMER R, KENAGY J. Will disruptive innovations cure health care?[J]. Harvard business review, 2000, 78(5):102.
[3] BOWER J L, CHRISTENSEN C M. Disruptive technologies:catching the wave[J]. Harvard business review, 1995, 73(1):43-53.
[4] ASSINK M. Inhibitors of disruptive innovation capability:a conceptual model[J]. European journal of innovation management, 2006, 9(2):215-233.
[5] DANNEELS E. Disruptive technology reconsidered:a critique and research agenda[J]. Journal of product innovation management, 2004, 21(4):246-258.
[6] FUNK J L. Mobile disruption:the technologies and applications driving the mobile internet[M]. New York:Wiley, 2005.
[7] KENAGY J W, CHRISTENSEN C M. Disruptive innovation:a new diagnosis for health care's "Financial Flu"[J]. Healthcare financial management, 2002, 56(5):62-66.
[8] THOMOND P, LETTICE F. Disruptive innovation explored[C]//9th IPSE international conference on concurrent engineering:research and applications. Cranfield:Cranfield University, 2002:17-28.
[9] MARKIDES C. Disruptive innovation:in need of better theory[J]. Journal of product innovation management, 2006, 23(1):19-25.
[10] 陈继祥,王敏. 破坏性创新理论最新研究综述[J]. 科技进步与对策, 2009, 26(11):155-160.
[11] 陈劲,柳御林. 自主创新与国家强盛[M]. 北京:科学出版社, 2010.
[12] 付玉秀,张洪石. 突破性创新:概念界定与比较[J]. 数量经济技术经济研究, 2004(3):73-83.
[13] 许轶,李婧,许海云,等. 基于专利动态特征的技术转移潜力识别研究[J]. 农业图书情报学报, 2021, 33(6):107-115.
[14] 苏鹏,苏成,潘云涛. 颠覆性技术识别方法发展现状及启示[J]. 图书情报工作, 2019, 63(20):129-138.
[15] 张金柱,王秋月,仇蒙蒙. 颠覆性技术识别研究进展综述[J].数据分析与知识发现,2022,6(7):12-31.
[16] BUCHANAN B, CORKEN R. A toolkit for the systematic analysis of patent data to assess a potentially disruptive technology[EB/OL].[2022-06-04]. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/312333/informatic-techtoolkit.pdf.
[17] MOMENI A, ROST K. Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling[J]. Technological forecasting and social change, 2016, 104:16-29.
[18] 苏敬勤,刘建华,王智琦,等. 颠覆性技术的演化轨迹及早期识别——以智能手机等技术为例[J]. 科研管理, 2016, 37(3):13-20.
[19] 白光祖,郑玉荣,吴新年,等. 基于文献知识关联的颠覆性技术预见方法研究与实证[J]. 情报杂志,2017, 36(9):38-44.
[20] JIA W, XIE Y, ZHAO Y, et al. Research on disruptive technology recognition of China's electronic information and communication industry based on patent influence[J]. Journal of global information management, 2021, 29(2):148-165.
[21] DOTSIKA F, WATKINS A. Identifying potentially disruptive trends by means of keyword network analysis[J]. Technological forecasting & social change, 2017, 119:114-127.
[22] ARTS S, PAOLO A F, LOOY B. Inventions shaping technological trajectories:do existing patent indicators provide a comprehensive picture?[J]. Scientometrics, 2013, 97(2):397-419.
[23] 赵格. 基于多源异构数据的颠覆性技术识别[D]. 武汉:华中科技大学, 2017.
[24] 庞弘燊,方曙,范炜,等. 基于多重共现的机构科研状况分析方法研究——以中科院国家科学图书馆为例[J]. 情报学报,2012, 31(11):1140-1152.
[25] 庞弘燊. 基于科技论文多特征项共现突发强度分析方法的算法实现与可视化图谱研究[J]. 图书情报工作,2015, 59(24):115-122.
[26] 王静静. 基于论文和专利的技术机会分析[D]. 北京:北京工业大学, 2016.
[27] 陈云鹏,张明,高燕,等. 标准引用与论文引用、专利引用的异同比较研究[J]. 标准科学, 2015, 12:15-19.
[28] XU H Y, YUE Z H, PANG H S, et al. Integrative model for discovering linked topics in science and technology[J]. Journal of informetrics, 2022, 16(2):101265.
[29] XU H, WINNINK J, YUE Z, et al. Multidimensional scientometric indicators for the detection of emerging research topics[J]. Technological forecasting and social change, 2021, 163:120490.
[30] XU H, WINNINK J, YUE Z, et al. Topic-linked innovation paths in science and technology[J]. Journal of informetrics, 2020, 14(2):101014.
[31] NARIN F, NOMA E. Is technology becoming science[J]. Scientometrics, 1985, 7(3/6):369-381.
[32] SCHMOCH U. Tracing the knowledge transfer from science to technology as reflected in patent indicators[J]. Scientometrics, 1993, 26(1):193-211.
[33] GLANZEL W, MEYER M. Patents cited in the scientific literature:an exploratory study of ‘reverse’ citation relations[J]. Scientometrics, 2003, 58(2):415-428.
[34] MEYER M, DEBACKERE K, GLANZEL W. Can applied science be ‘good science’? exploring the relationship between patent citations and citation impact in nanoscience[J]. Scientometrics, 2010, 85(2):527-539.
[35] SHIBATA N, KAJIKAWA Y, SAKATA I. Extracting the commercialization gap between science and technology-case study of a solar cell[J]. Technological forecasting and social change, 2010, 77(7):1147-1155.
[36] WANG G, GUAN J. 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.
[37] 张俊. 科学与技术的关联性研究[D]. 南京:南京农业大学, 2014.
[38] 王俏. 基于专利文献与科技论文组合分析的抗焦虑药领域现状与热点探测[D].长春:吉林大学, 2015.
[39] 殷媛媛,肖沪卫. 基于论文专利的科学技术互动发展趋势研究——以立体显示产业为例[J]. 情报杂志, 2011, 30(6):25-29.
[40] 高继平,丁堃,滕立,等. 专利-论文混合共被引网络下的知识流动探析[J]. 科学学研究, 2011, 29(8):1184-1189.
[41] 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.
[42] 高继平,姚长青,马峥,等. 论文专利互引分析方法的应用及其不足——在科学技术关联分析方面[J]. 科学学与科学技术管理, 2014, 35(12):39-44.
[43] 张玲玲,张宇娥,杜丽. 基于期刊文献与专利文献的科学技术互关联研究方法对比分析[J]. 情报杂志, 2017, 36(7):116-121.
[44] 庞弘燊. 基于科技文献多重共现的数据模型理论与知识发现应用范例研究[J]. 图书情报工作, 2019, 63(9):61-72.
[45] KELLER A, HUSIG S. Ex ante identification of disruptive innovations in the software industry applied to Web applications:the case of microsoft's vs. Google's office applications[J]. Technological forecasting and social change, 2009, 76(8):1044-1054.
[46] PANETTA K. 5 trends appear on the gartner hype cycle for emerging technologies, 2019[EB/OL].[2022-06-29]. https://www.gartner.com/smarterwithgartner/5-trends-appear-on-the-gartner-hype-cycle-for-emerging-technologies-2019.
[47] 张金柱,张晓林. 基于专利科学引文的突破性创新识别研究述评[J]. 情报学报, 2016, 35(9):955-962.
[48] 杨艳萍,董瑜,韩涛. 基于专利共被引聚类和组合分析的产业关键技术识别方法研究——以作物育种技术为例[J]. 图书情报工作, 2016, 60(19):143-148.
[49] 王秀红,高敏. 基于BERT-LDA的关键技术识别方法及其实证研究——以农业机器人为例[J]. 图书情报工作, 2021, 65(22):114-125.
[50] 巨乃岐,刘冠军. 从科学与技术的关系看科学向技术转化的实质与要求[J]. 河南大学学报(社会科学版), 2004(1):24-28.
[51] GARDNER P. Representations of the relationship between science and technology in the curriculum[J]. Studies in science education, 1994, 24(1):1-28.
[52] 刘自强,许海云,罗瑞,等. 基于主题关联分析的科技互动模式识别方法研究[J]. 情报学报, 2019, 38(10):997-1011.
[53] HUANG M, YANG H, CHEN D. 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.
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