融合文本和引用特征的科学技术互动社区识别研究
Identification of Science and Technology Interaction Communities by Fusing Tex-tual and Citation Characteristics of Papers and Patents
王嘉杰 1侯万方 1马亚雪 1孙建军1
作者信息
- 1. 南京大学数据智能与交叉创新实验室,南京,210023;南京大学信息管理学院,南京,210023
- 折叠
摘要
科学与技术间的良好互动模式是催生重大创新的关键,针对以论文和专利为代表的科技创新成果,探索融合文本和引用特征的科学技术互动社区识别方法,有助于研究人员和创新管理者深入理解科学技术互动模式、优化科技创新成果转化和发现科技交叉创新路径.本研究基于文本表示学习、图自编码器和相似性网络融合等算法,提出一种融合论文和专利的文本和引用特征的科学技术互动社区识别方法,并从互动社区的互动内容和互动强度角度对特定领域的科学技术互动情况进行全面分析;选取基因工程疫苗领域进行实证分析,并设置对比实验验证该方法的有效性.结果显示,所识别到的科学技术互动社区能够有效描述领域内科学技术互动情况,展示领域内科技交叉创新热点以及互动演化情况,还原科学技术互动社区的发展脉络,为科学技术互动研究提供全新的知识单元和应用场景.
Abstract
A good interaction pattern between science and technology is the key to generating major inno-vations,and exploring the identification method of science and technology interaction community fusing text and citation characteristics for scientific and technological innovations represented by papers and patents will help researchers and innovation managers to understand the interaction pattern of science and technology,optimize the transformation of scientific and technological innovations,and discover the path of scientific and technological cross-innovation.Based on the algorithms of text representation learning,graph autoencoder(GAE)and similarity network fusion(SNF),this study proposes a method to identify science and technology interaction communities by fusing textual and citation characteristics of papers and patents,and comprehen-sively analyzes the science and technology interactions in a specific field from the dimensions of content and intensity of interaction communities.In this study,the field of genetically engineered vaccines is selected for empirical analysis,and the effectiveness of the method is verified through comparative experiments.The re-sults show that the science and technology interaction communities identified in this study can effectively de-scribe the science and technology interaction situation in the field,demonstrate the hotspots of scientific and technological cross-innovation in the field as well as the evolution of interaction,restore the development of the science and technology interaction communities,and provide brand new knowledge units and application scenarios for the study of science and technology interaction.
关键词
科学技术互动/社区发现/图自编码器/文本表示学习/网络融合Key words
Science and technology interaction/Community identification/Graph auto-encoder/Text representation learning/Network fusion引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据