Mapping the technological landscape of green smart buildings: A patent analytics of key topics, leading companies, and technology gaps

Lee, Yong-Jae

doi:10.1016/j.jobe.2024.111020

Journal of Building Engineering

2024

DOI: 10.1016/j.jobe.2024.111020

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Mapping the technological landscape of green smart buildings: A patent analytics of key topics, leading companies, and technology gaps

Yong-Jae Lee

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2024

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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

Lee,

Park,

Lee

2024

Sustainability

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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.

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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

Lee,

Park,

Lee

2024

Sustainability

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Mapping the technological landscape of green smart buildings: A patent analytics of key topics, leading companies, and technology gaps

Cited by 1 publication

References 80 publications

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

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

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