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

Jihoo Jung, Jehyun Lee (), Sangjin Choi and Woonho Baek
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Jihoo Jung: Global Strategy Team, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
Jehyun Lee: Computational Science & Engineering Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
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

Energies, 2022, vol. 16, issue 1, 1-17

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.

Keywords: open API; international cooperation; data analysis; R&D planning; text mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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