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Predicting future technological convergence patterns based on machine learning using link prediction

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  • Joon Hyung Cho

    (Yonsei University)

  • Jungpyo Lee

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

Technological convergence among different industries is an important source of innovation and economic growth. In this study, we propose a new framework for predicting patterns of technological convergence in two different industries. We first construct an inter-process communication co-occurrence network based on association rule mining. We then use a machine learning approach with various link prediction indices to predict future technological convergence patterns. Next, we use latent Dirichlet allocation (LDA) topic modeling to identify the keywords associated with technologies that are predicted to converge. We apply our proposed framework to a dataset of patents from the United States Patent and Trademark Office from 2012 to 2014 in the fields of chemical engineering and environmental technology. The empirical analysis results show that the prediction over a 4-year time interval using the random forest model achieves the highest performance. Moreover, the LDA topic modeling results indicate that the keywords “membrane,” “air,” “separation,” “catalyst,” “gas,” “exhaust,” and “particle” are descriptions of technologies that are likely to converge. This study is expected to contribute to technological and economic growth by predicting new technological fields that are likely to emerge in the future, and hence the directions that firms focusing on technological advancement should prepare for.

Suggested Citation

  • Joon Hyung Cho & Jungpyo Lee & So Young Sohn, 2021. "Predicting future technological convergence patterns based on machine learning using link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5413-5429, July.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:7:d:10.1007_s11192-021-03999-8
    DOI: 10.1007/s11192-021-03999-8
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    as
    1. Andrew Rodriguez & Byunghoon Kim & Mehmet Turkoz & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong, 2015. "New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 565-581, May.
    2. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    3. Martin, Ben R. & Nightingale, Paul & Yegros-Yegros, Alfredo, 2012. "Science and technology studies: Exploring the knowledge base," Research Policy, Elsevier, vol. 41(7), pages 1182-1204.
    4. Hu, Rui & Skea, Jim & Hannon, Matthew J., 2018. "Measuring the energy innovation process: An indicator framework and a case study of wind energy in China," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 227-244.
    5. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    6. Sherkat, Ehsan & Rahgozar, Maseud & Asadpour, Masoud, 2015. "Structural link prediction based on ant colony approach in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 80-94.
    7. Seongkyoon Jeong & Jong-Chan Kim & Jae Young Choi, 2015. "Technology convergence: What developmental stage are we in?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 841-871, September.
    8. P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
    9. Fredrik Hacklin & Martin W. Wallin, 2013. "Convergence and interdisciplinarity in innovation management: a review, critique, and future directions," The Service Industries Journal, Taylor & Francis Journals, vol. 33(7-8), pages 774-788, May.
    10. Tommaso Antonucci & Mario Pianta, 2002. "Employment Effects of Product and Process Innovation in Europe," International Review of Applied Economics, Taylor & Francis Journals, vol. 16(3), pages 295-307.
    11. Iwai, Katsuhito, 2000. "A contribution to the evolutionary theory of innovation, imitation and growth," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 167-198, October.
    12. Baruffaldi, Stefano H. & Simeth, Markus, 2020. "Patents and knowledge diffusion: The effect of early disclosure," Research Policy, Elsevier, vol. 49(4).
    13. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    14. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    15. Dolata, Ulrich, 2009. "Technological innovations and sectoral change: Transformative capacity, adaptability, patterns of change: An analytical framework," Research Policy, Elsevier, vol. 38(6), pages 1066-1076, July.
    16. William P. Jones & George W. Furnas, 1987. "Pictures of relevance: A geometric analysis of similarity measures," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 38(6), pages 420-442, November.
    17. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    18. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    19. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
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    4. Li Yao & He Ni, 2023. "Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4933-4969, September.

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