Scientific Breakthrough Topics Identification in an Early Stage Using Multiple Weak Linkage Fusion
Liu Yahui1,2, Xu Haiyun3,4, Wu Huawei5, Liu Chunjiang6, Wang Haiyan4
1.National Science Library, Chinese Academy of Sciences, Beijing 100190 2.Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 3.Business School, Shandong University of Technology, Zibo 255000 4.Institute of Scientific and Technical Information of China, Beijing 100038 5.Archives of Northwest Normal University, Lanzhou 730070 6.Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu 610041
Abstract By the term “scientific breakthroughs,” we mean innovations that are relatively transformative and have a profound impact on the future direction and trends of a disciplinary field. A scientific breakthrough aims to reveal new phenomena and laws that have emerged earlier but have not yet been recognized or investigated formally. Identifying scientific breakthroughs at an earlier stage can provide decision support to policymakers and granting agencies in optimizing resource allocation. This study selected the field of Gene Engineered Vaccine for empirical research—focusing on weak association linkages in knowledge networks—and constructed a multi-layer network based on subject term co-occurrence, author co-authorship, and reference co-citation relationships. Thereafter, we analyzed the association information between multiple feature items to mine the subject content and evaluated the recognition effect with the help of expert judgment and authoritative reports. The comparative analysis with the identification results based on strong correlation relationships verified that the method constructed in this study is applicable to the early identification of scientific breakthroughs. Future research could draw on network representation learning and introduce temporal networks to pinpoint the moment when a breakthrough is made to fade away.
Liu Yahui,Xu Haiyun,Wu Huawei, et al. Scientific Breakthrough Topics Identification in an Early Stage Using Multiple Weak Linkage Fusion[J]. 情报学报, 2023, 42(1): 19-30.
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