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Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage

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  • Dejian Yu

    (Nanjing Audit University)

  • Zhaoping Yan

    (Nanjing Audit University)

Abstract

With the development of the era of big data, research data are accumulating, and various research directions emerge endlessly. It is difficult for researchers to grasp the hotspots and development trends of the discipline. Therefore, exploring methods to quickly and accurately identify research fronts is of great significance to scientific and technological innovation. This paper proposes a research front identification method integrating machine learning and main path analysis in conjunction with papers and patents based on the existing research. The innovation of this method is the combination of citation analysis and semantic analysis to identify research front from the perspective of science-technology linkage. This article takes the Internet of Things in supply chain as an example to verify the feasibility and effectiveness of the method. It is of great significance to identify important scientific and technological research fronts in a specific domain by intuitively revealing knowledge diffusion and text mining. The proposed method enriches the application of MPA and helps scholars grasp the latest information, mainstreams and future directions.

Suggested Citation

  • Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:7:d:10.1007_s11192-022-04443-1
    DOI: 10.1007/s11192-022-04443-1
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    2. Qiang Gao & Man Jiang, 2024. "Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4043-4070, July.

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