基于多元弱关系融合的科学突破主题早期识别研究  

Scientific Breakthrough Topics Identification in an Early Stage Using Multiple Weak Linkage Fusion

作　　者：刘亚辉 许海云 武华维[5] 刘春江 王海燕[4] Liu Yahui;Xu Haiyun;Wu Huawei;Liu Chunjiang;Wang Haiyan(National Science Library,Chinese Academy of Sciences,Beijing 100190;Department of Library,Information and Archives Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190;Business School,Shandong University of Technology,Zibo 255000;Institute of Scientific and Technical Information of China,Beijing 100038;Archives of Northwest Normal University,Lanzhou 730070;Chengdu Documentation and Information Center,Chinese Academy of Sciences,Chengdu 610041)

机构地区：[1]中国科学院文献情报中心,北京100190 [2]中国科学院大学经济与管理学院图书情报与档案管理系,北京100190 [3]山东理工大学管理学院,淄博255000 [4]中国科学技术信息研究所,北京100038 [5]西北师范大学档案馆,兰州730070 [6]中国科学院成都文献情报中心,成都610041

出　　处：《情报学报》2023年第1期19-30,共12页Journal of the China Society for Scientific and Technical Information

基　　金：国家自然科学基金项目“基于弱信号时效网络演化分析的变革性科技创新主题早期识别方法研究”(72274113);国家重点研发计划项目课题“颠覆性技术地平线扫描系统”(2019YFA0707202-01);山东省泰山学者工程项目(202103069)。

摘　　要：科学突破是变革程度较高、对领域未来发展方向和趋势影响较为深远的科学研究,旨在揭示早期尚未被认识到或重视起来的新现象、新规律。较早阶段识别科学突破主题能够为政策制定者和基金资助机构提供决策支持,有助于优化资源配置。本文选取基因工程疫苗领域展开实证研究,重点关注知识网络中的弱关联关系,构建基于主题词共现-作者合著-参考文献同被引等关系的多层网络,通过综合分析多个特征项之间的关联信息挖掘主题内容,并借助专家判断和权威报告评估识别效果。与基于强关联关系的识别结果的对比分析,验证了本文构建方法适用于科学突破主题的早期识别。在未来研究中可以借鉴网络表示学习,引入时效网络精准定位到某项突破从产生到消退的时间点。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.

关 键 词：弱关系 科学突破 突破性创新 多元关系融合 早期识别 

分 类 号：G353.1[文化科学—情报学] G301