Measuring knowledge complexity in the biomedical domain based on a question-method knowledge representation model

Abstract

In nowadays knowledge-driven economy, knowledge complexity plays a crucial role in gaining a competitive advantage. In the biomedical field, this complexity spurs innovation and enables resource monopolization. Previous studies on knowledge complexity have primarily examined the interactions between knowledge units and nonknowledge systems from a macro perspective. These analyses often overlook how the micro-level components of knowledge influence its overall complexity. This study marks a departure from such approaches by conceptualizing biomedical knowledge in terms of questions and methods, as well as proposing a novel method to measure knowledge complexity. This approach emphasizes the exploration of connections between knowledge units by constructing a question-method bipartite network. The validity of our methodology was rigorously tested through controlled experiments involving random networks and a comprehensive review of the relevant literature. Furthermore, this study reveals the relationship between knowledge complexity and dissemination, suggesting that the more complex knowledge is, the more likely it will be cited frequently. Internal knowledge flows within the same research question exhibit greater sensitivity to knowledge complexity than external flows. This study can help demystify the sophisticated scientific knowledge system and provides detailed insights into the complexity of scientific knowledge and dissemination mechanisms.