IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i8d10.1007_s11192-021-04000-2.html
   My bibliography  Save this article

An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field

Author

Listed:
  • Hong Wu

    (Shandong University of Technology)

  • Huifang Yi

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

  • Chang Li

    (Shandong University of Technology)

Abstract

Comprehensive, in-depth and accurate analyses of patent technology topic evolutions become increasingly significant since the analytical results can offer related personnel the scientific support to explore or trace back to the origin and the development of the technology. However, existing methods of topic evolutions do not facilitate better understanding of how a technology topic has evolved. This paper introduces an integrated method with the LDA topic identification analysis, the improved topic life cycle analysis, and the improved technology entropy analysis for identifying, measuring and interpreting topics evolutions from patent literatures. Multiple indicators we proposed and improved have been used to measure the degree of topic development and identify the topic types of different states. And, the concept of technology entropy has been redefined and improved to measure the changes of evolution intensity and evolution direction among topics, mainly used the topic word and its probability. The results from different methods are mutually connected and complemented. The process and characteristics of topic evolution are further overviewed. Graphene is selected for the case study. The mechanism of evolution and the effect of improved methods are focused on. The research has clearly shown that more accurate and comprehensive results can be achieved for topic evolution by employing this integrated method. Furthermore, the above integration of methods has potential contributions to hot spot detection and potential technology discovery.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04000-2
    DOI: 10.1007/s11192-021-04000-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04000-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04000-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jia Zheng & Zhi-yun Zhao & Xu Zhang & Dar-zen Chen & Mu-hsuan Huang & Xiao-ping Lei & Ze-yu Zhang & Yun-hua Zhao & Run-sheng Liu, 2011. "Industry evolution and key technologies in China based on patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(1), pages 175-188, April.
    2. Chen, Ssu-Han & Huang, Mu-Hsuan & Chen, Dar-Zen, 2012. "Identifying and visualizing technology evolution: A case study of smart grid technology," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1099-1110.
    3. 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.
    4. 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.
    5. Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila & An Yan, 2014. "Triple Helix innovation in China’s dye-sensitized solar cell industry: hybrid methods with semantic TRIZ and technology roadmapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(1), pages 55-75, April.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Junsheng Zhang & Xiaoping Sun & Zhihui Liu, 2024. "Measuring the evolving stage of temporal distribution of research topic keyword in scientific literature by research heat curve," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7287-7328, November.
    2. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    3. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
    4. Wencan Tian & Yongzhen Wang & Zhigang Hu & Ruonan Cai & Guangyao Zhang & Xianwen Wang, 2024. "Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3285-3302, June.
    5. 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).
    6. Hengmin Zhu & Li Qian & Wang Qin & Jing Wei & Chao Shen, 2022. "Evolution analysis of online topics based on ‘word-topic’ coupling network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3767-3792, July.
    7. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3313-3335, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sanghoon Lee & Wonjoon Kim, 2017. "The knowledge network dynamics in a mobile ecosystem: a patent citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 717-742, May.
    2. Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
    3. 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).
    4. Munan Li, 2015. "A novel three-dimension perspective to explore technology evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1679-1697, December.
    5. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    6. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    7. ZHU Chen & MOTOHASHI Kazuyuki, 2022. "Government R&D spending as a driving force of technology convergence," Discussion papers 22030, Research Institute of Economy, Trade and Industry (RIETI).
    8. Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 2017. "Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 210-220.
    9. 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).
    10. 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.
    11. Arenal, Alberto & Armuña, Cristina & Feijoo, Claudio & Ramos, Sergio & Xu, Zimu & Moreno, Ana, 2020. "Innovation ecosystems theory revisited: The case of artificial intelligence in China," Telecommunications Policy, Elsevier, vol. 44(6).
    12. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    13. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    14. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    15. Weiwei Liu & Yuan Tao & Zhile Yang & Kexin Bi, 2019. "Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    16. Slavo Radosevic & Esin Yoruk, 2014. "Are there global shifts in the world science base? Analysing the catching up and falling behind of world regions," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1897-1924, December.
    17. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    18. Sabrina L. Woltmann & Lars Alkærsig, 2018. "Tracing university–industry knowledge transfer through a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 449-472, 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.
    20. Kim, Namil & Lee, Hyeokseong & Kim, Wonjoon & Lee, Hyunjong & Suh, Jong Hwan, 2015. "Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data," Research Policy, Elsevier, vol. 44(9), pages 1734-1748.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04000-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.