Tree knowledge structure for better insight: Capturing biomedical science-technology knowledge linkage with MeSH

Measuring the knowledge linkage between science and technology (S&T) is crucial for understanding the interactions between S&T and assisting decision-makers in strategizing research and development investments. Conventional analyses of S&T knowledge linkage have frequently overlooked the semantic structure of knowledge elements thereby introducing biases in the measurements. To address this issue, this study introduces a novel method predicated on the tree semantic structure, which quantifies the S&T linkage by considering the hierarchy and category of knowledge elements within an ontological framework. In this method, knowledge trees are constructed to represent the core knowledge of S&T literature, incorporating hierarchically organized MeSH descriptors. These knowledge trees are subsequently utilized to measure the knowledge linkage between S&T by integrating intra-branch knowledge similarity and inter-branch knowledge distribution. An empirical analysis was conducted on a substantial corpus of scientific publications and patents within the biomedicine sector. The findings predominantly revealed a stronger knowledge linkage between S&T in recent years, relative to the early 2000 s. It was also observed that patents are more inclined to include broader concepts in their titles and abstracts, in contract to the more specific concepts found in scientific publications. S&T literatures have increasingly focused on knowledge related to diseases, equipment, and health care. To verify the reliability of the proposed method, validation was performed with alternative measurements of knowledge linkage. In comparison to single-feature-based linkage measurements and network-based approaches, our proposed method demonstrates superior adaptability in capturing S&T linkage, especially when there is a marked disparity in the sample sizes of S&T literature. This study not only enriches the measurements of S&T knowledge linkage, but also furnishes empirical insights into the evolving patterns of S&T linkage within the biomedical domain.