Affiliation:
1. Nanjing University People's Republic of China
Abstract
ABSTRACTUnderstanding the interactions between science and technology (S&T) is crucial for driving major innovations. Previous studies have typically focused on identifying scientific and technical topics separately and analyzing their association through semantic or citation. In this study, we propose a novel approach to identifying linked topics that directly reflect the interactions within the S&T domain. Our approach integrates semantic characteristics and citation relationships, allowing for a comprehensive analysis of the specific content and structure of these interactions. We test our approach using a dataset of 2,821 patents and 4,626 papers from the field of genetic engineering vaccines, spanning the years 1980 to 2020. The results demonstrate that our approach provides a more direct and detailed understanding of the content and structural characteristics of S&T interactions. This research contributes to the methodology of linked topics identification in the field of S&T, offering new insights and analytical perspectives for related studies.
Subject
Library and Information Sciences,General Computer Science