IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v101y2014i2d10.1007_s11192-014-1293-8.html
   My bibliography  Save this article

Identifying SCI covered publications within non-patent references in U.S. utility patents

Author

Listed:
  • Masashi Shirabe

    (Tokyo Institute of Technology)

Abstract

In order to evaluate approaches for identifying science citation index (SCI) covered publications within non-patent references (NPRs), the author employs a computer science method that uses two key indicators, recall and precision, to evaluate the relevance of information retrieval systems. There are two primary reasons that this method is adequate: in contrast to the retrievability ratios used previously, first, this method can evaluate two dimensions of matching accuracy, and second, results of its evaluation are independent of the intermediate outcome. The author then proposes an approach for identifying SCI publications within NPRs that consists of five steps: (1) data collection, (2) creation of supervised and test data, (3) selection and execution of matching algorithms, (4) evaluation of algorithms and optimization of their combinations, and (5) evaluation of optimized combinations. A comparison of the proposed and conventional approaches showed that the proposed approach works well, with results far better (99 % precision and 95 % recall) than the target implicitly set in previous studies. The author also applied the approach to comprehensive NPR data in U.S. utility patents registered between 1992 and 2012 and checked the performance. Results showed that the approach could identify SCI publications from within millions of NPRs in an acceptable time (i.e., within a couple of weeks) and that it performs as expected from the evaluation in step 5. On the basis of these results, the proposed approach is considered of value in studies on relations and/or interactions between science publications and patents.

Suggested Citation

  • Masashi Shirabe, 2014. "Identifying SCI covered publications within non-patent references in U.S. utility patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 999-1014, November.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1293-8
    DOI: 10.1007/s11192-014-1293-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1293-8
    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/s11192-014-1293-8?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. Meyer, Martin, 2000. "Does science push technology? Patents citing scientific literature," Research Policy, Elsevier, vol. 29(3), pages 409-434, March.
    2. van Vianen, B. G. & Moed, H. F. & van Raan, A. F. J., 1990. "An exploration of the science base of recent technology," Research Policy, Elsevier, vol. 19(1), pages 61-81, February.
    3. Julie Callaert & Joris Grouwels & Bart Looy, 2012. "Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 383-398, May.
    4. Bruno Cassiman & Reinhilde Veugelers & Pluvia Zuniga, 2008. "In search of performance effects of (in)direct industry science links," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 17(4), pages 611-646, August.
    5. Martin Meyer, 2000. "What is Special about Patent Citations? Differences between Scientific and Patent Citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 49(1), pages 93-123, August.
    6. Julie Callaert & Bart Van Looy & Arnold Verbeek & Koenraad Debackere & Bart Thijs, 2006. "Traces of Prior Art: An analysis of non-patent references found in patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 3-20, October.
    7. Julie Callaert & Maikel Pellens & Bart Looy, 2014. "Sources of inspiration? Making sense of scientific references in patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1617-1629, March.
    8. 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. Chul Lee & Gunno Park & Klaus Marhold & Jina Kang, 2017. "Top management team’s innovation-related characteristics and the firm’s explorative R&D: an analysis based on patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 639-663, May.
    2. Matt Marx & Aaron Fuegi, 2020. "Reliance on science: Worldwide front‐page patent citations to scientific articles," Strategic Management Journal, Wiley Blackwell, vol. 41(9), pages 1572-1594, September.
    3. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.

    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. Leonardo Costa Ribeiro & Glenda Kruss & Gustavo Britto & Américo Tristão Bernardes & Eduardo Motta e Albuquerque, 2014. "A methodology for unveiling global innovation networks: patent citations as clues to cross border knowledge flows," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 61-83, October.
    2. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).
    3. Veugelers, Reinhilde & Wang, Jian, 2019. "Scientific novelty and technological impact," Research Policy, Elsevier, vol. 48(6), pages 1362-1372.
    4. Chul Lee & Gunno Park & Klaus Marhold & Jina Kang, 2017. "Top management team’s innovation-related characteristics and the firm’s explorative R&D: an analysis based on patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 639-663, May.
    5. Ugo Rizzo & Nicolò Barbieri & Laura Ramaciotti & Demian Iannantuono, 2020. "The division of labour between academia and industry for the generation of radical inventions," The Journal of Technology Transfer, Springer, vol. 45(2), pages 393-413, April.
    6. Breschi, Stefano & Catalini, Christian, 2010. "Tracing the links between science and technology: An exploratory analysis of scientists' and inventors' networks," Research Policy, Elsevier, vol. 39(1), pages 14-26, February.
    7. Acosta, Manuel & Coronado, Daniel, 2003. "Science-technology flows in Spanish regions: An analysis of scientific citations in patents," Research Policy, Elsevier, vol. 32(10), pages 1783-1803, December.
    8. Julie Callaert & Joris Grouwels & Bart Looy, 2012. "Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 383-398, May.
    9. Leten, Bart & Landoni, Paolo & Van Looy, Bart, 2014. "Science or graduates: How do firms benefit from the proximity of universities?," Research Policy, Elsevier, vol. 43(8), pages 1398-1412.
    10. Naomi Fukuzawa & Takanori Ida, 2016. "Science linkages between scientific articles and patents for leading scientists in the life and medical sciences field: the case of Japan," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 629-644, February.
    11. Ding, Cherng G. & Hung, Wen-Chi & Lee, Meng-Che & Wang, Hung-Jui, 2017. "Exploring paper characteristics that facilitate the knowledge flow from science to technology," Journal of Informetrics, Elsevier, vol. 11(1), pages 244-256.
    12. Beck, Mathias & Junge, Martin & Kaiser, Ulrich, 2017. "Public Funding and Corporate Innovation," IZA Discussion Papers 11196, Institute of Labor Economics (IZA).
    13. 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).
    14. 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.
    15. Matt Marx & Aaron Fuegi, 2020. "Reliance on science: Worldwide front‐page patent citations to scientific articles," Strategic Management Journal, Wiley Blackwell, vol. 41(9), pages 1572-1594, September.
    16. Basse Mama, Houdou, 2018. "Nonlinear capital market payoffs to science-led innovation," Research Policy, Elsevier, vol. 47(6), pages 1084-1095.
    17. Kenneth Zahringer & Christos Kolympiris & Nicholas Kalaitzandonakes, 2017. "Academic knowledge quality differentials and the quality of firm innovation," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(5), pages 821-844.
    18. Julie Callaert & Maikel Pellens & Bart Looy, 2014. "Sources of inspiration? Making sense of scientific references in patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1617-1629, March.
    19. Leten, Bart & Kelchtermans, Stijn & Belderbos, Ren, 2010. "Internal Basic Research, External Basic Research and the Technological Performance of Pharmaceutical Firms," Working Papers 2010/12, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    20. Tijssen, Robert J. W., 2001. "Global and domestic utilization of industrial relevant science: patent citation analysis of science-technology interactions and knowledge flows," Research Policy, Elsevier, vol. 30(1), pages 35-54, January.

    More about this item

    Keywords

    Non-patent references; U.S. utility patents; Science citation; Automated matching;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    Statistics

    Access and download statistics

    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:101:y:2014:i:2:d:10.1007_s11192-014-1293-8. 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.