IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v17y2023i4s1751157723000755.html
   My bibliography  Save this article

Measuring the novelty of scientific publications: A fastText and local outlier factor approach

Author

Listed:
  • Jeon, Daeseong
  • Lee, Junyoup
  • Ahn, Joon Mo
  • Lee, Changyong

Abstract

Although the novelty of scientific publications has been the subject of previous studies, most have examined the distribution of references in the bibliography, which may not be effective in capturing implied scientific knowledge. We propose an analytical framework for measuring the novelty of scientific publications using a paper's title. At the heart of the framework, fastText is used to construct a vector space model in which papers with similar scientific knowledge are located close to each other, and the local outlier factor is used to measure the novelty of scientific knowledge implied in the papers on a numerical scale. The feasibility and validity of the analytical framework were assessed by comparing the average novelty scores of papers recommended with novelty-related tags in Faculty Opinions to those of papers without such tags. This case study of 15,653 papers published in a biomedical journal confirms that our framework is a useful complementary tool for the continuous assessment of the novelty of scientific publications and can serve as a starting point for developing more general models.

Suggested Citation

  • Jeon, Daeseong & Lee, Junyoup & Ahn, Joon Mo & Lee, Changyong, 2023. "Measuring the novelty of scientific publications: A fastText and local outlier factor approach," Journal of Informetrics, Elsevier, vol. 17(4).
    • Handle: RePEc:eee:infome:v:17:y:2023:i:4:s1751157723000755
    DOI: 10.1016/j.joi.2023.101450
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157723000755
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2023.101450?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. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    2. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    3. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    4. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    5. Hamid R. Jamali & Mahsa Nikzad, 2011. "Article title type and its relation with the number of downloads and citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 653-661, August.
    6. Kuniko Matsumoto & Sotaro Shibayama & Byeongwoo Kang & Masatsura Igami, 2021. "Introducing a novelty indicator for scientific research: validating the knowledge-based combinatorial approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6891-6915, August.
    7. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    8. Veugelers, Reinhilde & Wang, Jian, 2019. "Scientific novelty and technological impact," Research Policy, Elsevier, vol. 48(6), pages 1362-1372.
    9. Manuel Trajtenberg & Rebecca Henderson & Adam Jaffe, 1997. "University Versus Corporate Patents: A Window On The Basicness Of Invention," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 5(1), pages 19-50.
    10. Sotaro Shibayama & Deyun Yin & Kuniko Matsumoto, 2021. "Measuring novelty in science with word embedding," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-16, July.
    11. Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).
    12. Kim, Jieun & Lee, Changyong, 2017. "Novelty-focused weak signal detection in futuristic data: Assessing the rarity and paradigm unrelatedness of signals," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 59-76.
    13. Luo, Zhuoran & Lu, Wei & He, Jiangen & Wang, Yuqi, 2022. "Combination of research questions and methods: A new measurement of scientific novelty," Journal of Informetrics, Elsevier, vol. 16(2).
    14. Dag W. Aksnes, 2006. "Citation rates and perceptions of scientific contribution," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(2), pages 169-185, January.
    15. Tahamtan, Iman & Bornmann, Lutz, 2018. "Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references?," Journal of Informetrics, Elsevier, vol. 12(3), pages 906-930.
    16. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    17. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    18. Wang, Jian, 2014. "Unpacking the Matthew effect in citations," Journal of Informetrics, Elsevier, vol. 8(2), pages 329-339.
    19. Lee, You-Na & Walsh, John P. & Wang, Jian, 2015. "Creativity in scientific teams: Unpacking novelty and impact," Research Policy, Elsevier, vol. 44(3), pages 684-697.
    20. Lee, Changyong & Kim, Juram & Kwon, Ohjin & Woo, Han-Gyun, 2016. "Stochastic technology life cycle analysis using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 53-64.
    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. Jeon, Daeseong & Ahn, Joon Mo & Kim, Juram & Lee, Changyong, 2022. "A doc2vec and local outlier factor approach to measuring the novelty of patents," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Kim, Juram & Hong, Suckwon & Kang, Yubin & Lee, Changyong, 2023. "Domain-specific valuation of university technologies using bibliometrics, Jonckheere–Terpstra tests, and data envelopment analysis," Technovation, Elsevier, vol. 122(C).
    4. Yan Yan & Shanwu Tian & Jingjing Zhang, 2020. "The impact of a paper’s new combinations and new components on its citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 895-913, February.
    5. Yulin Yu & Daniel M. Romero, 2024. "Does the Use of Unusual Combinations of Datasets Contribute to Greater Scientific Impact?," Papers 2402.05024, arXiv.org, revised Sep 2024.
    6. Yuefen Wang & Lipeng Fan & Lei Wu, 2024. "A validation test of the Uzzi et al. novelty measure of innovation and applications to collaboration patterns between institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4379-4394, July.
    7. Zhaoping Yan & Kaiyu Fan, 2024. "An integrated indicator for evaluating scientific papers: considering academic impact and novelty," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6909-6929, November.
    8. Hong, Suckwon & Kim, Juram & Woo, Han-Gyun & Kim, Young-Choon & Lee, Changyong, 2022. "Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach," Technovation, Elsevier, vol. 112(C).
    9. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    10. Kuniko Matsumoto & Sotaro Shibayama & Byeongwoo Kang & Masatsura Igami, 2021. "Introducing a novelty indicator for scientific research: validating the knowledge-based combinatorial approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6891-6915, August.
    11. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    12. Sotaro Shibayama & Jian Wang, 2020. "Measuring originality in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 409-427, January.
    13. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    14. Sam Arts & Nicola Melluso & Reinhilde Veugelers, 2023. "Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication Text," Papers 2309.16437, arXiv.org, revised Dec 2024.
    15. Zhentao Liang & Jin Mao & Gang Li, 2023. "Bias against scientific novelty: A prepublication perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 99-114, January.
    16. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    17. Lee, Gyumin & Lee, Sungjun & Lee, Changyong, 2023. "Inventor–licensee matchmaking for university technology licensing: A fastText approach," Technovation, Elsevier, vol. 125(C).
    18. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    19. Dongqing Lyu & Kaile Gong & Xuanmin Ruan & Ying Cheng & Jiang Li, 2021. "Does research collaboration influence the “disruption” of articles? Evidence from neurosciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 287-303, January.
    20. Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).

    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:eee:infome:v:17:y:2023:i:4:s1751157723000755. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.