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Funding map using paragraph embedding based on semantic diversity

Author

Listed:
  • Takahiro Kawamura

    (Japan Science and Technology Agency)

  • Katsutaro Watanabe

    (Japan Science and Technology Agency)

  • Naoya Matsumoto

    (Japan Science and Technology Agency)

  • Shusaku Egami

    (Japan Science and Technology Agency)

  • Mari Jibu

    (Japan Science and Technology Agency)

Abstract

Maps of science representing the structure of science can help us understand science and technology (S&T) development. Studies have thus developed techniques for analyzing research activities’ relationships; however, ongoing research projects and recently published papers have difficulty in applying inter-citation and co-citation analysis. Therefore, in order to characterize what is currently being attempted in the scientific landscape, this paper proposes a new content-based method of locating research projects in a multi-dimensional space using the recent word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with the original paragraph vectors, we introduce paragraph vectors based on the information entropies of concepts in an S&T thesaurus. The experimental results show that the proposed method successfully formed a clustered map from 25,607 project descriptions of the 7th Framework Programme of EU from 2006 to 2016 and 34,192 project descriptions of the National Science Foundation from 2012 to 2016.

Suggested Citation

  • Takahiro Kawamura & Katsutaro Watanabe & Naoya Matsumoto & Shusaku Egami & Mari Jibu, 2018. "Funding map using paragraph embedding based on semantic diversity," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 941-958, August.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2783-x
    DOI: 10.1007/s11192-018-2783-x
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    References listed on IDEAS

    as
    1. Klavans, Richard & Boyack, Kevin W., 2017. "Research portfolio analysis and topic prominence," Journal of Informetrics, Elsevier, vol. 11(4), pages 1158-1174.
    2. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    3. Richard Klavans & Kevin W. Boyack, 2017. "Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 984-998, April.
    4. Shenghui Wang & Rob Koopman, 2017. "Clustering articles based on semantic similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1017-1031, May.
    5. Ahlgren, Per & Colliander, Cristian, 2009. "Document–document similarity approaches and science mapping: Experimental comparison of five approaches," Journal of Informetrics, Elsevier, vol. 3(1), pages 49-63.
    6. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co-citation clustering using full text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
    7. 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.
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    Cited by:

    1. Kamal Sanguri & Atanu Bhuyan & Sabyasachi Patra, 2020. "A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 233-269, October.
    2. Yi Zhang & Fen Zhao & Jianguo Lu, 2019. "P2V: large-scale academic paper embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 399-432, October.

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