  Novel scientific knowledge is constantly produced by the scientific
community. Understanding the level of novelty characterized by scientific
literature is key for modeling scientific dynamics and analyzing the growth
mechanisms of scientific knowledge. Metrics derived from bibliometrics and
citation analysis were effectively used to characterize the novelty in
scientific 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 scientific community over a specific 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
scientific literature by temporal embedding learning techniques. We validated
the effects of the proposed novelty metric on predicting the future growth of
scientific publications and investigated the relations between novelty and
growth by panel data analysis applied in a large-scale publication dataset
(MEDLINE/PubMed). Key findings based on the statistical investigation indicate
that the novelty metric has significant predictive effects on the growth of
scientific 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.
