IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v125y2020i2d10.1007_s11192-020-03664-6.html
   My bibliography  Save this article

Using ontologies to map between research data and policymakers’ presumptions: the experience of the KNOWMAK project

Author

Listed:
  • Diana Maynard

    (University of Sheffield)

  • Benedetto Lepori

    (Università della Svisera italiana
    University of Paris Est)

  • Johann Petrak

    (University of Sheffield)

  • Xingyi Song

    (University of Sheffield)

  • Philippe Laredo

    (University of Paris Est)

Abstract

Understanding knowledge co-creation in key emerging areas of European research is critical for policy makers wishing to analyze impact and make strategic decisions. However, purely data-driven methods for characterising policy topics have limitations relating to the broad nature of such topics and the differences in language and topic structure between the political language and scientific and technological outputs. In this paper, we discuss the use of ontologies and semantic technologies as a means to bridge the linguistic and conceptual gap between policy questions and data sources for characterising European knowledge production. Our experience suggests that the integration between advanced techniques for language processing and expert assessment at critical junctures in the process is key for the success of this endeavour.

Suggested Citation

  • Diana Maynard & Benedetto Lepori & Johann Petrak & Xingyi Song & Philippe Laredo, 2020. "Using ontologies to map between research data and policymakers’ presumptions: the experience of the KNOWMAK project," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1275-1290, November.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:2:d:10.1007_s11192-020-03664-6
    DOI: 10.1007/s11192-020-03664-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03664-6
    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/s11192-020-03664-6?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. Peter van den Besselaar & Gaston Heimeriks, 2006. "Mapping research topics using word-reference co-occurrences: A method and an exploratory case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 377-393, September.
    2. Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
    3. Ismael Rafols & Alan L. Porter & Loet Leydesdorff, 2010. "Science overlay maps: A new tool for research policy and library management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(9), pages 1871-1887, September.
    4. Robert P. Light & David E. Polley & Katy Börner, 2014. "Open data and open code for big science of science studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1535-1551, November.
    5. Cinzia Daraio & Maurizio Lenzerini & Claudio Leporelli & Henk F. Moed & Paolo Naggar & Andrea Bonaccorsi & Alessandro Bartolucci, 2016. "Data integration for research and innovation policy: an Ontology-Based Data Management approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 857-871, February.
    6. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    7. Cassi, Lorenzo & Lahatte, Agénor & Rafols, Ismael & Sautier, Pierre & de Turckheim, Élisabeth, 2017. "Improving fitness: Mapping research priorities against societal needs on obesity," Journal of Informetrics, Elsevier, vol. 11(4), pages 1095-1113.
    8. Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
    9. Benedetto Lepori & Rémi Barré & Ghislaine Filliatreau, 2008. "New perspectives and challenges for the design and production of S&T indicators," Research Evaluation, Oxford University Press, vol. 17(1), pages 33-44, March.
    10. Abdullah Gök & Alec Waterworth & Philip Shapira, 2015. "Use of web mining in studying innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 653-671, January.
    11. Frietsch, Rainer & Neuhäusler, Peter & Rothengatter, Oliver & Jonkers, Koen, 2016. "Societal Grand Challenges from a technological perspective: Methods and identification of classes of the International Patent Classification IPC," Discussion Papers "Innovation Systems and Policy Analysis" 53, Fraunhofer Institute for Systems and Innovation Research (ISI).
    12. Loet Leydesdorff & Ismael Rafols, 2009. "A global map of science based on the ISI subject categories," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 348-362, February.
    Full references (including those not matched with items on IDEAS)

    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. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    2. Jochen Gläser & Wolfgang Glänzel & Andrea Scharnhorst, 2017. "Same data—different results? Towards a comparative approach to the identification of thematic structures in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 981-998, May.
    3. Paul Donner, 2021. "Validation of the Astro dataset clustering solutions with external data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1619-1645, February.
    4. Keungoui Kim & Dieter F. Kogler & Sira Maliphol, 2024. "Identifying interdisciplinary emergence in the science of science: combination of network analysis and BERTopic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    5. Chiara Carusi & Giuseppe Bianchi, 2020. "A look at interdisciplinarity using bipartite scholar/journal networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 867-894, February.
    6. Frank Havemann & Jochen Gläser & Michael Heinz, 2017. "Memetic search for overlapping topics based on a local evaluation of link communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1089-1118, May.
    7. Carlos Olmeda-Gómez & Carlos Romá-Mateo & Maria-Antonia Ovalle-Perandones, 2019. "Overview of trends in global epigenetic research (2009–2017)," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1545-1574, June.
    8. Rafols, Ismael & Leydesdorff, Loet & O’Hare, Alice & Nightingale, Paul & Stirling, Andy, 2012. "How journal rankings can suppress interdisciplinary research: A comparison between Innovation Studies and Business & Management," Research Policy, Elsevier, vol. 41(7), pages 1262-1282.
    9. Pitambar Gautam & Ryuichi Yanagiya, 2012. "Reflection of cross-disciplinary research at Creative Research Institution (Hokkaido University) in the Web of Science database: appraisal and visualization using bibliometry," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 101-111, October.
    10. Ying Huang & Wolfgang Glänzel & Lin Zhang, 2021. "Tracing the development of mapping knowledge domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6201-6224, July.
    11. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.
    12. Yuxian Liu & Ewelina Biskup & Yueqian Wang & Fengfeng Cai & Xiaoyan Zhang, 2020. "A new territory and its pioneer: opening up a dominant research stream for a translational research area," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1213-1228, November.
    13. Cassi, Lorenzo & Lahatte, Agénor & Rafols, Ismael & Sautier, Pierre & de Turckheim, Élisabeth, 2017. "Improving fitness: Mapping research priorities against societal needs on obesity," Journal of Informetrics, Elsevier, vol. 11(4), pages 1095-1113.
    14. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
    15. Matthias Held & Grit Laudel & Jochen Gläser, 2021. "Challenges to the validity of topic reconstruction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4511-4536, May.
    16. Rahman, A.I.M. Jakaria & Guns, Raf & Rousseau, Ronald & Engels, Tim C.E., 2015. "Is the expertise of evaluation panels congruent with the research interests of the research groups: A quantitative approach based on barycenters," Journal of Informetrics, Elsevier, vol. 9(4), pages 704-721.
    17. Wallace, Matthew L. & Ràfols, Ismael, 2018. "Institutional shaping of research priorities: A case study on avian influenza," Research Policy, Elsevier, vol. 47(10), pages 1975-1989.
    18. 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.
    19. Yan, Erjia & Ding, Ying & Cronin, Blaise & Leydesdorff, Loet, 2013. "A bird's-eye view of scientific trading: Dependency relations among fields of science," Journal of Informetrics, Elsevier, vol. 7(2), pages 249-264.
    20. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2018. "Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 869-892, May.

    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:125:y:2020:i:2:d:10.1007_s11192-020-03664-6. 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.