Exploring science-technology linkages: A deep learning-empowered solution

• Published in: Information processing & management Vol. 60; no. 2; p. 103255
• Main Authors: Chen, Xiang, Ye, Peifeng, Huang, Lu, Wang, Changtian, Cai, Yijie, Deng, Lijie, Ren, Hang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023
• Subjects: BERT; Deep learning; Network analysis; Node2Vec; Science-technology linkages; Deep learning; BERT; Science-technology linkages; Network analysis; Node2Vec
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/j.ipm.2022.103255

•A novel deep learning-based approach to gage science-technology linkages.•Information of knowledge structure and textual semantics is integrated.•S&T topic linkages identification task is successfully transferred into a bipartite matching problem. In-depth exploration of the knowledge linkages between science and technology (S&T) is an essential prerequisite for accurately understanding the S&T innovation laws, promoting the transformation of scientific outcomes, and optimizing S&T innovation policies. A novel deep learning-based methodology is proposed to investigate S&T linkages, where papers and patents are applied to represent science and technology. In order to accurately and comprehensively reveal the linkages between science and technology topics, the proposed framework combines the information of knowledge structure with textual semantics. Furthermore, the exploration analysis is also conducted from the perspective of realizing the optimal matching between science and technology topics, which can realize combinatorial optimization of the S&T knowledge systems. Specifically, science and technology networks are constructed based on Node2Vec and BERT. Then, science and technology topics are identified based on the Fast Unfolding algorithm and Z-Score index. Finally, a science-technology bipartite graph is constructed, the S&T topic linkages identification task is successfully transferred into a bipartite matching problem, and the maximum-weight matching is identified using a Kuhn-Munkres bipartite algorithm. An experiment on natural language processing demonstrates the feasibility and reliability of the proposed methodology.