IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i9d10.1007_s11192-021-04085-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04085-9
    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/s11192-021-04085-9?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. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    2. Shuo Xu & Junwan Liu & Dongsheng Zhai & Xin An & Zheng Wang & Hongshen Pang, 2018. "Overlapping thematic structures extraction with mixed-membership stochastic blockmodel," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 61-84, October.
    3. Calero-Medina, Clara & Noyons, Ed C.M., 2008. "Combining mapping and citation network analysis for a better understanding of the scientific development: The case of the absorptive capacity field," Journal of Informetrics, Elsevier, vol. 2(4), pages 272-279.
    4. 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).
    5. 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.
    6. James Hartley & James W. Pennebaker & Claire Fox, 2003. "Abstracts, introductions and discussions: How far do they differ in style?," Scientometrics, Springer;Akadémiai Kiadó, vol. 57(3), pages 389-398, July.
    7. 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).
    8. Brooks, Harvey, 1994. "The relationship between science and technology," Research Policy, Elsevier, vol. 23(5), pages 477-486, September.
    9. Wolfgang Glänzel & Martin Meyer, 2003. "Patents cited in the scientific literature: An exploratory study of 'reverse' citation relations," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(2), pages 415-428, October.
    10. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    11. Arnold Verbeek & Koenraad Debackere & Marc Luwel & Petra Andries & Edwin Zimmermann & Filip Deleus, 2002. "Linking science to technology: Using bibliographic references in patents to build linkage schemes," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(3), pages 399-420, July.
    12. 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).
    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. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
    15. Ji-ping Gao & Kun Ding & Li Teng & Jie Pang, 2012. "Hybrid documents co-citation analysis: making sense of the interaction between science and technology in technology diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 459-471, November.
    16. Dubaric, Ervin & Giannoccaro, Dimitris & Bengtsson, Rune & Ackermann, Thomas, 2011. "Patent data as indicators of wind power technology development," World Patent Information, Elsevier, vol. 33(2), pages 144-149, June.
    17. Chao Lu & Yi Bu & Jie Wang & Ying Ding & Vetle Torvik & Matthew Schnaars & Chengzhi Zhang, 2019. "Examining scientific writing styles from the perspective of linguistic complexity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(5), pages 462-475, May.
    18. Huang, Mu-Hsuan & Yang, Hsiao-Wen & Chen, Dar-Zen, 2015. "Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents," Journal of Informetrics, Elsevier, vol. 9(2), pages 237-249.
    19. Shuo Xu & Liyuan Hao & Xin An & Dongsheng Zhai & Hongshen Pang, 2019. "Types of DOI errors of cited references in Web of Science with a cleaning method," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1427-1437, September.
    20. Marcelo A. Montemurro & Damián H. Zanette, 2010. "Towards The Quantification Of The Semantic Information Encoded In Written Language," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 135-153.
    21. Narin, Francis & Hamilton, Kimberly S. & Olivastro, Dominic, 1997. "The increasing linkage between U.S. technology and public science," Research Policy, Elsevier, vol. 26(3), pages 317-330, October.
    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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. Shuo Xu & Ling Li & Xin An, 2023. "Do academic inventors have diverse interests?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1023-1053, February.

    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. Wang, Jean J. & Ye, Fred Y., 2021. "Probing into the interactions between papers and patents of new CRISPR/CAS9 technology: A citation comparison," Journal of Informetrics, Elsevier, vol. 15(4).
    2. 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).
    3. Xiaozan Lyu & Ping Zhou & Loet Leydesdorff, 2020. "Eco-system mapping of techno-science linkages at the level of scholarly journals and fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2037-2055, September.
    4. 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).
    5. Shuo Xu & Ling Li & Xin An, 2023. "Do academic inventors have diverse interests?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1023-1053, February.
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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.
    11. Xiaoling Sun & Kun Ding, 2018. "Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1735-1748, September.
    12. Yashuang Qi & Na Zhu & Yujia Zhai & Ying Ding, 2018. "The mutually beneficial relationship of patents and scientific literature: topic evolution in nanoscience," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 893-911, May.
    13. 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.
    14. Meyer, Martin, 2006. "Are patenting scientists the better scholars?: An exploratory comparison of inventor-authors with their non-inventing peers in nano-science and technology," Research Policy, Elsevier, vol. 35(10), pages 1646-1662, December.
    15. 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).
    16. Huang, Mu-Hsuan & Yang, Hsiao-Wen & Chen, Dar-Zen, 2015. "Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents," Journal of Informetrics, Elsevier, vol. 9(2), pages 237-249.
    17. 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).
    18. 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).
    19. Shen, Yung-Chi & Wang, Ming-Yeu & Yang, Ya-Chu, 2020. "Discovering the potential opportunities of scientific advancement and technological innovation: A case study of smart health monitoring technology," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    20. Wolfgang Glänzel & Martin Meyer, 2003. "Patents cited in the scientific literature: An exploratory study of 'reverse' citation relations," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(2), pages 415-428, October.

    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:9:d:10.1007_s11192-021-04085-9. 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.