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A novel method for topic linkages between scientific publications and patents

Author

Listed:
  • Shuo Xu
  • Dongsheng Zhai
  • Feifei Wang
  • Xin An
  • Hongshen Pang
  • Yirong Sun

Abstract

It is increasingly important to build topic linkages between scientific publications and patents for the purpose of understanding the relationships between science and technology. Previous studies on the linkages mainly focus on the analysis of nonpatent references on the front page of patents, or the resulting citation‐link networks, but with unsatisfactory performance. In the meanwhile, abundant mentioned entities in the scholarly articles and patents further complicate topic linkages. To deal with this situation, a novel statistical entity‐topic model (named the CCorrLDA2 model), armed with the collapsed Gibbs sampling inference algorithm, is proposed to discover the hidden topics respectively from the academic articles and patents. In order to reduce the negative impact on topic similarity calculation, word tokens and entity mentions are grouped by the Brown clustering method. Then a topic linkages construction problem is transformed into the well‐known optimal transportation problem after topic similarity is calculated on the basis of symmetrized Kullback–Leibler (KL) divergence. Extensive experimental results indicate that our approach is feasible to build topic linkages with more superior performance than the counterparts.

Suggested Citation

  • Shuo Xu & Dongsheng Zhai & Feifei Wang & Xin An & Hongshen Pang & Yirong Sun, 2019. "A novel method for topic linkages between scientific publications and patents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(9), pages 1026-1042, September.
    • Handle: RePEc:bla:jinfst:v:70:y:2019:i:9:p:1026-1042
    DOI: 10.1002/asi.24175
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    Citations

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    Cited by:

    1. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. 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.
    3. Yan Qi & Xin Zhang & Zhengyin Hu & Bin Xiang & Ran Zhang & Shu Fang, 2022. "Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5519-5550, September.
    4. 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).
    5. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).
    6. Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    7. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
    8. Shin, Hyunjin & Woo, Hyun Goo & Sohn, Kyung-Ah & Lee, Sungjoo, 2023. "Comparing research trends with patenting activities in the biomedical sector: The case of dementia," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    9. Shuo Xu & Ling Li & Xin An, 2023. "Do academic inventors have diverse interests?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1023-1053, February.
    10. Yang, Guancan & Lu, Guoxuan & Xu, Shuo & Chen, Liang & Wen, Yuxin, 2023. "Which type of dynamic indicators should be preferred to predict patent commercial potential?," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    11. 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.
    12. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    13. An, Xin & Li, Jinghong & Xu, Shuo & Chen, Liang & Sun, Wei, 2021. "An improved patent similarity measurement based on entities and semantic relations," Journal of Informetrics, Elsevier, vol. 15(2).
    14. Ba, Zhichao & Liang, Zhentao, 2021. "A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling," Journal of Informetrics, Elsevier, vol. 15(3).
    15. Ba, Zhichao & Meng, Kai & Ma, Yaxue & Xia, Yikun, 2024. "Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

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