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Mapping the technology evolution path: a novel model for dynamic topic detection and tracking

Author

Listed:
  • Huailan Liu

    (Huazhong University of Science and Technology)

  • Zhiwang Chen

    (Huazhong University of Science and Technology)

  • Jie Tang

    (Tsinghua University)

  • Yuan Zhou

    (Tsinghua University)

  • Sheng Liu

    (Huazhong University of Science and Technology)

Abstract

Identifying the evolution path of a research field is essential to scientific and technological innovation. There have been many attempts to identify the technology evolution path based on the topic model or social networks analysis, but many of them had deficiencies in methodology. First, many studies have only considered a single type of information (text or citation information) in scientific literature, which may lead to incomplete technology path mapping. Second, the number of topics in each period cannot be determined automatically, making dynamic topic tracking difficult. Third, data mining methods fail to be effectively combined with visual analysis, which will affect the efficiency and flexibility of mapping. In this study, we developed a method for mapping the technology evolution path using a novel non-parametric topic model, the citation involved Hierarchical Dirichlet Process (CIHDP), to achieve better topic detection and tracking of scientific literature. To better present and analyze the path, D3.js is used to visualize the splitting and fusion of the evolutionary path. We used this novel model to mapping the artificial intelligence research domain, through a successful mapping of the evolution path, the proposed method’s validity and merits are shown. After incorporating the citation information, we found that the CIHDP can be mapping a complete path evolution process and had better performance than the Hierarchical Dirichlet Process and LDA. This method can be helpful for understanding and analyzing the development of technical topics. Moreover, it can be well used to map the science or technology of the innovation ecosystem. It may also arouse the interest of technology evolution path researchers or policymakers.

Suggested Citation

  • Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03700-5
    DOI: 10.1007/s11192-020-03700-5
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    References listed on IDEAS

    as
    1. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
    2. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    3. Wanying Ding & Chaomei Chen, 2014. "Dynamic topic detection and tracking: A comparison of HDP, C-word, and cocitation methods," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(10), pages 2084-2097, October.
    4. Yuan Zhou & Xin Li & Rasmus Lema & Frauke Urban, 2016. "Comparing the knowledge bases of wind turbine firms in Asia and Europe: Patent trajectories, networks, and globalisation," Science and Public Policy, Oxford University Press, vol. 43(4), pages 476-491.
    5. Yuan Zhou & Meijuan Pan & Frauke Urban, 2018. "Comparing the International Knowledge Flow of China’s Wind and Solar Photovoltaic (PV) Industries: Patent Analysis and Implications for Sustainable Development," Sustainability, MDPI, vol. 10(6), pages 1-34, June.
    6. Jeong, Do-Heon & Song, Min, 2014. "Time gap analysis by the topic model-based temporal technique," Journal of Informetrics, Elsevier, vol. 8(3), pages 776-790.
    7. Tibor Braun & Wolfgang Glänzel & András Schubert, 2001. "Publication and cooperation patterns of the authors of neuroscience journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(3), pages 499-510, July.
    8. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    9. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    10. Tibor Braun & Wolfgang Glänzel & András Schubert, 2001. "Publication and cooperation patterns of the authors of neuroscience journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 499-510, January.
    11. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    12. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Liu, Ziqiang & Yuan, Guoting, 2020. "Topic-linked innovation paths in science and technology," Journal of Informetrics, Elsevier, vol. 14(2).
    13. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    14. Kong, Dejing & Zhou, Yuan & Liu, Yufei & Xue, Lan, 2017. "Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 80-97.
    15. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.
    16. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
    17. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    18. Guannan Xu & Weijie Hu & Yuanyuan Qiao & Yuan Zhou, 2020. "Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2057-2081, September.
    19. Nordensvard, Johan & Zhou, Yuan & Zhang, Xiao, 2018. "Innovation core, innovation semi-periphery and technology transfer: The case of wind energy patents," Energy Policy, Elsevier, vol. 120(C), pages 213-227.
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    Cited by:

    1. Moerchel, Alexander & Tietze, Frank & Urmetzer, Florian, 2023. "Visualising dynamics in innovation ecosystems: A new method and demonstration in the commercial aircraft MRO ecosystem," Technovation, Elsevier, vol. 128(C).
    2. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    3. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).

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