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A visualization tool of patent topic evolution using a growing cell structure neural network

Author

Listed:
  • Hui-Yun Sung

    (National Chung Hsing University)

  • Hsi-Yin Yeh

    (Policy Research Division)

  • Jin-Kwan Lin

    (Ming Chi University of Technology)

  • Ssu-Han Chen

    (Ming Chi University of Technology)

Abstract

This research used a cell structure map to visualize technological evolution and showed the developmental trend in a technological field. The basic concept was to organize patents into a map produced by growing cell structures. The map was then disassembled into clusters with similar contexts using the Girvan–Newman algorithm. Next, the continuity between clusters in two snapshots was identified and used as the base for establishing a trajectory in the technology. An analysis of patents in the flaw detection field found that the field was composed of several technological trajectories. Among them, ultrasonic flaw detection, wafer inspection and substrate inspection were relatively larger and more continuing technologies, while infrared thermography defect inspection has been an emerging topic in recent years. It is to be hoped that the map of technology constructed in this research provides insights into the history of technological evolution and helps explain the transition patterns through changes in cluster continuity. This can serve a reference point by experts who attempt to visualize the mapping of technological development or identify the latest focus of attention.

Suggested Citation

  • Hui-Yun Sung & Hsi-Yin Yeh & Jin-Kwan Lin & Ssu-Han Chen, 2017. "A visualization tool of patent topic evolution using a growing cell structure neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1267-1285, June.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2361-7
    DOI: 10.1007/s11192-017-2361-7
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    References listed on IDEAS

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    1. Chaomei Chen & Timothy Cribbin & Robert Macredie & Sonali Morar, 2002. "Visualizing and tracking the growth of competing paradigms: Two case studies," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(8), pages 678-689.
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    5. Mu-Hsuan Huang & Ssu-Han Chen & Chia-Ying Lin & Dar-Zen Chen, 2014. "Exploring temporal relationships between scientific and technical fronts: a case of biotechnology field," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1085-1100, February.
    6. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
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    Cited by:

    1. Hong Wu & Huifang Yi & Chang Li, 2021. "An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6301-6321, August.
    2. Yonghe Lu & Xin Xiong & Weiting Zhang & Jiaxin Liu & Ruijie Zhao, 2020. "Research on classification and similarity of patent citation based on deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 813-839, May.
    3. Xie, Qing & Zhang, Xinyuan & Ding, Ying & Song, Min, 2020. "Monolingual and multilingual topic analysis using LDA and BERT embeddings," Journal of Informetrics, Elsevier, vol. 14(3).
    4. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.

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