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Topic-linked innovation paths in science and technology

Author

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  • Xu, Haiyun
  • Winnink, Jos
  • Yue, Zenghui
  • Liu, Ziqiang
  • Yuan, Guoting

Abstract

In the modern world, science and technology jointly determine the evolutionary path of scientific innovation, with an increasingly close relationship between them. Therefore, it is important to study the identification method of the innovation path, based on the linkage of topics in science and technology. This study focuses on connected topics utilizing bibliometric analysis, thereby exploring the identification method for innovation paths based on the linkage of scientific and technological topics. The internal mechanism of knowledge dissemination and the relationship between science and technology are revealed and described in detail by measuring the linkage of knowledge units. For practical bibliometric analyses, research papers and patent literature were used to characterize scientific research and technological research to reveal the innovation path for the interaction of science and technology quantitatively, automatically, and visually. Experimental study shows that analysis of the topic-linked path of science and technology, along with the integration of multi-relationships, can effectively identify important science- and technology-related topics in a field in the evolution process, and help grasp the key points of basic research and applied research.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:2:s175115771930210x
    DOI: 10.1016/j.joi.2020.101014
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    4. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    5. 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.
    6. Mila Cascajares & Alfredo Alcayde & José Antonio Garrido-Cardenas & Francisco Manzano-Agugliaro, 2020. "The Contribution of Spanish Science to Patents: Medicine as Case of Study," IJERPH, MDPI, vol. 17(10), pages 1-24, May.
    7. Shuo Xu & Ling Li & Xin An & Liyuan Hao & Guancan Yang, 2021. "An approach for detecting the commonality and specialty between scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7445-7475, September.
    8. 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.
    9. Kang, Inje & Yang, Jiseong & Lee, Wonjae & Seo, Eun-Yeong & Lee, Duk Hee, 2023. "Delineating development trends of nanotechnology in the semiconductor industry: Focusing on the relationship between science and technology by employing structural topic model," Technology in Society, Elsevier, vol. 74(C).
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