Journal
NANOMATERIALS
Volume 12, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/nano12050838
Keywords
nanogenerator; technology evolution pathway; knowledge graph; representation learning; multi-source data
Categories
Funding
- National Natural Science Foundation of China [72104224, 71974107, L2124002, 91646102, L1924058]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences [16JDGC011]
- Construction Project of China Knowledge Center for Engineering Sciences and Technology [CKCEST-2021-2-7]
- Tsinghua University Initiative Scientific Research Program [2019Z02CAU]
- Tsinghua University Project of Volvo-Supported Green Economy and Sustainable Development [20183910020]
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This study proposes a novel framework for analyzing the evolutionary pathways of advanced technologies in the emerging field of nanogenerators. By calculating the similarity between clusters of different layers, the evolutionary pathways from grants to papers and then to patents are drawn, monitoring the development of established technologies and identifying emerging technologies under research.
As an emerging nano energy technology, nanogenerators have been developed rapidly, which makes it crucial to analyze the evolutionary pathways of advanced technology in this field to help estimate the development trend and direction. However, some limitations existed in previous studies. On the one hand, previous studies generally made use of the explicit correlation of data such as citation and cooperation between patents and papers, which ignored the rich semantic information contained in them. On the other hand, the progressive evolutionary process from scientific grants to academic papers and then to patents was not considered. Therefore, this paper proposes a novel framework based on a separated three-layer knowledge graph with several time slices using grant data, paper data, and patent data. Firstly, by the representation learning method and clustering algorithm, several clusters representing specific technologies in different layers and different time slices can be obtained. Then, by calculating the similarity between clusters of different layers, the evolutionary pathways of advanced technology from grants to papers and then to patents is drawn. Finally, this paper monitors the pathways of some developed technologies, which evolve from grants to papers and then to patents, and finds some emerging technologies under research.
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