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

  • Xiao Zhou
  • Lu HuangEmail author
  • Yi Zhang
  • Miaomiao Yu
Article
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Abstract

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.

Keywords

Patent network analysis The structure of science revolutions Bibliometrics Text mining Technological recombination Artificial intelligence 

Notes

Acknowledgements

This work was supported by the National Science Foundation of China Funds (Grant No. 71774013 and 71673024), the National Science Foundation of China Yong Funds (Grant No. 71103015 and 71704139), the Basic Research Foundation of the Beijing Institute of Technology (Grant No. 20152142010), and the Special Items Fund of the Beijing Municipal Commission of Education. We also acknowledge Dr. Lu Yang for his contribution as an AI expert for this study.

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© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.School of Economics and ManagementXidian UniversityXi’anPeople’s Republic of China
  2. 2.School of Management and EconomicsBeijing Institute of TechnologyBeijingPeople’s Republic of China
  3. 3.Centre for Artificial Intelligence, Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia

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