A hybrid approach to detecting technological recombination based on text mining and patent network analysis

被引:38
作者
Zhou, Xiao [1 ]
Huang, Lu [2 ]
Zhang, Yi [3 ]
Yu, Miaomiao [2 ]
机构
[1] Xidian Univ, Sch Econ & Management, 266 Xinglong Sect,Xifeng Rd, Xian 710126, Shaanxi, Peoples R China
[2] Beijing Inst Technol, Sch Management & Econ, 5 South Zhong Guan Cun St, Beijing 100081, Peoples R China
[3] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia
基金
澳大利亚研究理事会;
关键词
Patent network analysis; The structure of science revolutions; Bibliometrics; Text mining; Technological recombination; Artificial intelligence; TECHNICAL INTELLIGENCE; KNOWLEDGE STRUCTURES; INNOVATION; SCIENCE; IMPACT; SIMILARITY; EVOLUTION; PATHWAYS; DYNAMICS; INTERNET;
D O I
10.1007/s11192-019-03218-5
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur's theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn's theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field-its communities, its technologies, and their interactions-is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.
引用
收藏
页码:699 / 737
页数:39
相关论文
共 101 条
[1]   Statistical mechanics of complex networks [J].
Albert, R ;
Barabási, AL .
REVIEWS OF MODERN PHYSICS, 2002, 74 (01) :47-97
[2]  
Alpaydin E, 2016, NEURAL NETWORKS DEEP, P217
[3]  
[Anonymous], 2016, CHINA TIMES
[4]  
Arjun K, 2016, JAPANS SOFTBANK BUY
[5]  
Arthur W. B., 2009, The Nature of Technology: What It Is and How It Evolves
[6]  
Babaeizadeh M., 2016, C INT C LEARN REPR I
[7]  
BAE HS, 2016, C IND ELECT APPL, V22, P1542
[8]   Genentech The Beginnings of Biotech [J].
Barnett, Gerald .
SCIENCE, 2011, 334 (6062) :1497-1497
[9]   Leadership in OM research: a social network analysis of European researchers [J].
Behara, Ravi S. ;
Babbar, Sunil ;
Smart, Philip Andrew .
INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT, 2014, 34 (12) :1537-1563
[10]  
Bunz M, 2017, ARTIFICIAL INTELLIGE