Early identification of breakthrough research from sleeping beauties using machine learning

Abstract

Breakthrough research is groundbreaking and transformative scientific research that can lead to new frontiers and even trigger substantial changes in the scientific paradigm. Early identification of breakthrough research is crucial for scientists, R&D experts, and policymakers. "Sleeping Beauty in Science" is a category of papers characterized as "delayed recognition", which is considered as the crucial carriers of breakthrough research. Machine learning methods can extract and learn high-quality information from a priori knowledge to predict future trends. In this paper, to address the shortcomings of existing studies on the early identification of breakthrough research, we propose a framework for identifying breakthrough research from sleeping beauties using machine learning. In this framework, we first construct machine learning models to obtain the relationship patterns between historical sleeping beauties and their citation trends. Then, we use these relational patterns to identify potential sleeping beauties. Secondly, we construct a breakthrough index based on the essential features of breakthrough research, then we apply it to identify breakthrough research among potential sleeping beauties, enabling the early identification of breakthrough research. Finally, an empirical study is conducted in the chemistry research field to verify the validity and flexibility of this framework. The results show that the framework can effectively identify breakthrough research from sleeping beauties. This paper contributes to the early identification of breakthrough research, evaluating academic results, and exploring research frontiers. Additionally, it will also provide methodological support for the decision-making of R&D experts and policymakers.