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Machine Learning-Driven Topic Modeling and Network Analysis to Uncover Shared Knowledge Networks for Sustainable Korea–Japan Intangible Cultural Heritage Cooperation

Author

Listed:
  • Yong-Jae Lee

    (Department of Industrial and Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea)

  • Sung-Eun Park

    (Division of Future Convergence (HCI Science Major), Dongduk Women’s University, Seoul 02748, Republic of Korea)

  • Seong-Yeob Lee

    (Graduate School of Management of Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

Abstract

In this study, we provide a comparative analysis of intangible cultural heritage (ICH) research trends in Korea and Japan, aiming to uncover shared knowledge networks and potential areas for sustainable cooperation. We employ a mixed-method approach, combining machine learning-driven topic modeling using Latent Dirichlet Allocation (LDA) and network analysis techniques, to examine a corpus of Korean and Japanese research papers on ICH. LDA topic modeling identified three primary themes: technology and ICH, safeguarding ICH, and methodologies and approaches in ICH research. Comparative analysis reveals distinct characteristics in each country’s approach. Korean research emphasizes practical applications of technology and policy-driven safeguarding strategies, while Japanese research leans towards theoretical exploration and cross-cultural comparisons. Citation network analysis further identifies influential papers and shared knowledge bases, underlining potential opportunities for collaboration. Key findings highlight the potential of technology for ICH preservation and promotion, the necessity of comprehensive safeguarding strategies, and the crucial role of community engagement. Our study suggests that by leveraging their complementary strengths and engaging in collaborative research, Korea and Japan can contribute to the sustainable safeguarding of ICH and foster a deeper understanding of their shared cultural heritage.

Suggested Citation

  • Yong-Jae Lee & Sung-Eun Park & Seong-Yeob Lee, 2024. "Machine Learning-Driven Topic Modeling and Network Analysis to Uncover Shared Knowledge Networks for Sustainable Korea–Japan Intangible Cultural Heritage Cooperation," Sustainability, MDPI, vol. 16(24), pages 1-38, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:10855-:d:1541580
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    References listed on IDEAS

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    1. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
    2. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    3. Liangchao Huang & Zhengmeng Hou & Yanli Fang & Jianhua Liu & Tianle Shi, 2023. "Evolution of CCUS Technologies Using LDA Topic Model and Derwent Patent Data," Energies, MDPI, vol. 16(6), pages 1-14, March.
    4. Seok Jin Youn & Yong-Jae Lee & Ha-Eun Han & Chang-Woo Lee & Donggyun Sohn & Chulung Lee, 2024. "A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems," Sustainability, MDPI, vol. 16(15), pages 1-32, August.
    5. Sixuan Liu & Younghwan Pan, 2023. "Exploring Trends in Intangible Cultural Heritage Design: A Bibliometric and Content Analysis," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    6. Seulah Kim & Dong-uk Im & Jongoh Lee & Heejae Choi, 2019. "Utility of Digital Technologies for the Sustainability of Intangible Cultural Heritage (ICH) in Korea," Sustainability, MDPI, vol. 11(21), pages 1-19, November.
    7. Zhen Li & Derrick Tate, 2015. "Automatic ontology generation from patents using a pre-built library, WordNet and a class-based n-gram model," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 20(2), pages 142-172.
    8. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    9. Sunwoo Park & Namho Chung & Won Seok Lee, 2020. "Preserving the Culture of Jeju Haenyeo (Women Divers) as a Sustainable Tourism Resource," Sustainability, MDPI, vol. 12(24), pages 1-11, December.
    10. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    11. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
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