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Information Analysis on Foreign Institution for International R&D Collaboration Using Natural Language Processing

Author

Listed:
  • Jihoo Jung

    (Global Strategy Team, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    These authors equally contributed to this study.)

  • Jehyun Lee

    (Computational Science & Engineering Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    These authors equally contributed to this study.)

  • Sangjin Choi

    (Global Strategy Team, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea)

  • Woonho Baek

    (Global Strategy Team, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea)

Abstract

The number of international collaborations in research and development (R&D) has been increasing in the energy sector to solve global environmental problems—such as climate change and the energy crisis—and to reduce the time, cost, and risk of failure. Successful international project planning requires the analysis of research fields and the technology expertise of cooperative partner institutions or countries, but this takes time and resources. In this study, we developed a method to analyze the information on research organizations and topics, taking advantage of data analysis as well as deep learning natural language processing (NLP) models. A method to evaluate the relative superiority of efficient international collaboration was suggested, assuming international collaboration of the National Renewable Energy Laboratory (NREL) and the Korea Institute of Energy Research (KIER). Additionally, a workflow of an automated executive summary and a translation of tens of web-posted articles is also suggested for a quick glance. The valuation of the suggested methodology is estimated as much as the annual salary of an experienced employee.

Suggested Citation

  • Jihoo Jung & Jehyun Lee & Sangjin Choi & Woonho Baek, 2022. "Information Analysis on Foreign Institution for International R&D Collaboration Using Natural Language Processing," Energies, MDPI, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:33-:d:1009473
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    References listed on IDEAS

    as
    1. Muhammad, Hafiz Ali & Cho, Junhyun & Cho, Jongjae & Choi, Bongsu & Roh, Chulwoo & Ishfaq, Hafiz Ahmad & Lee, Gilbong & Shin, Hyungki & Baik, Young-Jin & Lee, Beomjoon, 2022. "Performance improvement of supercritical carbon dioxide power cycle at elevated heat sink temperatures," Energy, Elsevier, vol. 239(PD).
    2. Junseok Lee & Ji-Ho Kang & Sunghae Jun & Hyunwoong Lim & Dongsik Jang & Sangsung Park, 2018. "Ensemble Modeling for Sustainable Technology Transfer," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    3. 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.
    4. Walter Leal Filho & Tony Wall & Jelena Barbir & Gabriela Nagle Alverio & Maria Alzira Pimenta Dinis & Julianna Ramirez, 2022. "Relevance of international partnerships in the implementation of the UN Sustainable Development Goals," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
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