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Novel scientic knowledge is constantly produced by the scientic community. Understanding the level of novelty characterized by scientic literature is key for modeling scientic dynamics and analyzing the growth mechanisms of scientic knowledge. Metrics derived from bibliometrics and citation analysis were effectively used to characterize the novelty in scientic development. However, time is required before we can observe links between documents such as citation links or patterns derived from the links, which makes these techniques more effective for retrospective analysis than predictive analysis. In this study, we present a new approach to measuring the novelty of a research topic in a scientic community over a specic period by tracking semantic changes of the terms and characterizing the research topic in their usage context. The semantic changes are derived from the text data of scientic literature by temporal embedding learning techniques. We validated the effects of the proposed novelty metric on predicting the future growth of scientic publications and investigated the relations between novelty and growth by panel data analysis applied in a large-scale publication dataset (MEDLINE/PubMed). Key ndings based on the statistical investigation indicate that the novelty metric has signicant predictive effects on the growth of scientic literature and the predictive effects may last for more than ten years. We demonstrated the effectiveness and practical implications of the novelty metric in three case studies.

Predictive Effects of Novelty Measured by Temporal Embeddings on the Growth of Scientific Literature