An Information Extraction Approach to Detecting Novelty of Biomedical Publications

Scientific novelty plays a critical role in shaping research impact, yet it remains inconsistently defined and difficult to quantify. Existing approaches often reduce novelty to a single measure, failing to distinguish the specific types of contributions (such as new concepts or relationships) that drive influence. In this study, we introduce a semantic measure of novelty based on the emergence of new biomedical entities and relationships within the conclusion sections of research articles. Leveraging transformer-based named entity recognition (NER) and relation extraction (RE) tools, we identify novel findings and classify articles into four categories: No Novelty, Entity-only Novelty, Relation-only Novelty, and Entity-Relation Novelty. We evaluate this framework using citation counts and Journal Impact Factors (JIF) as proxies for research influence. Our results show that Entity-Relation Novelty articles receive the highest citation impact, with relation novelty more closely aligned with high-impact journals. These findings offer a scalable framework for assessing novelty and guiding future research evaluation.