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An approach for detecting the commonality and specialty between scientific publications and patents

Author

Listed:
  • Shuo Xu

    (Beijing University of Technology)

  • Ling Li

    (Beijing University of Technology)

  • Xin An

    (Beijing Forestry University)

  • Liyuan Hao

    (Beijing University of Technology)

  • Guancan Yang

    (Renmin University of China)

Abstract

Scientific publications and patents are usually viewed as respective proxies of scientific research and technical development. There is considerable effort spent towards establishing topic linkages between science and technology with the lexical- or topic-based approaches. However, due to the heterogeneity between scholarly articles and patents in terms of purpose, statement, and quality, the performance is not satisfactory. To understand the difficulties of topic linkages and improve the performance, a framework is proposed to detect the commonality and specialty between scientific publications and patents from the two perspectives: linguistic characteristics and thematic structures. Extensive experimental results on the DrugBank dataset discover five commonness and five significant differences in terms of linguistic characteristics. For example, nouns are used most frequently among them, and scientific publications contain more word tokens than patent documents, but patents have usually longer sentences and use more clauses. In the meanwhile, common and special thematic structures are also uncovered between scientific publications and patents. The themes about general description in the pharmaceutical field are shared by two heterogeneous resources. The scientific publications tend to explain the disease mechanism and the medication content, while patents bias towards the preparation and practical application of drugs.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:9:d:10.1007_s11192-021-04085-9
    DOI: 10.1007/s11192-021-04085-9
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    References listed on IDEAS

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    Cited by:

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    3. 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).
    4. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    5. 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).

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