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Detecting technological recombination using semantic analysis and dynamic network analysis

Author

Listed:
  • Xiaoli Cao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xiang Chen

    (Beijing Institute of Technology)

  • Lu Huang

    (Beijing Institute of Technology)

  • Lijie Deng

    (Beijing Institute of Technology)

  • Yijie Cai

    (Beijing Institute of Technology)

  • Hang Ren

    (Beijing Institute of Technology)

Abstract

Technological recombinative innovation is a crucial way of innovation, and detecting technological recombination can effectively identify the technical elements with recombinative innovation potential in the future. This study proposes a novel method for detecting technological recombination by combining semantic analysis and dynamic network analysis. The framework accurately captures the hidden semantic changes behind keywords over time and deeply excavates the dynamic evolution characteristics of keyword networks in the development process, which effectively improves the accuracy of the identification results of technological recombination. Firstly, the dynamic word embedding model is applied to generate word vectors, and construct the dynamic keyword networks. Then, the dynamic network link prediction method is trained to predict the future network and the possibility of connection between keywords is calculated, which represents the technological recombination potential value. Finally, in order to identify potential recombination opportunities of crucial technologies in the field, SLM community detection is combined with the PageRank algorithm to identify core keywords in communities of the future network, and then technological recombination candidates corresponding to core keywords are detected. A case study on artificial intelligence domain demonstrates the reliability of the methodology.

Suggested Citation

  • Xiaoli Cao & Xiang Chen & Lu Huang & Lijie Deng & Yijie Cai & Hang Ren, 2024. "Detecting technological recombination using semantic analysis and dynamic network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7385-7416, November.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-023-04812-4
    DOI: 10.1007/s11192-023-04812-4
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