Development of Similarity Measures From Graph-Structured Bibliographic Metadata: An Application to Identify Scientific Convergence

Abstract

Scientific convergence is a phenomenon where the distance between hitherto distinct scientific fields narrows and the fields gradually overlap over time. It is creating important potential for research, development, and innovation. Although scientific convergence is crucial for the development of radically new technology, the identification of emerging scientific convergence is particularly difficult since the underlying knowledge flows are rather fuzzy and unstable in the early convergence stage. Nevertheless, novel scientific publications emerging at the intersection of different knowledge fields may reflect convergence processes. Thus, in this article, we exploit the growing number of research and digital libraries providing bibliographic metadata to propose an automated analysis of science dynamics. We utilize and adapt machine-learning methods (DeepWalk) to automatically learn a similarity measure between scientific fields from graphs constructed on bibliographic metadata. With a time-based perspective, we apply our approach to analyze the trajectories of evolving similarities between scientific fields. We validate the learned similarity measure by evaluating it within the well-explored case of cholesterol-lowering ingredients in which scientific convergence between the distinct scientific fields of nutrition and pharmaceuticals has partially taken place. Our results confirm that the similarity trajectories learned by our approach resemble the expected behavior, indicating that our approach may allow researchers and practitioners to detect and predict scientific convergence early.

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