Measuring the novelty of biomedical papers using the latent distances between knowledge units
TL;DR
This study introduces a comprehensive novelty measurement that incorporates three types of relationships between knowledge units: network, semantic, and hierarchical and demonstrates that combining all three distance metrics yields more effective identification of novel papers than using any single perspective alone.
Abstract
Measuring the novelty of scientific papers is a central concern in research evaluation and scientometrics. From a recombination perspective, prior studies have largely focused on the co-occurrence of knowledge units to assess the novelty of scientific papers. However, these studies often overlook other relationships between knowledge units. This narrow view may result in inaccurate or incomplete evaluations of novelty for scientific papers. To fill this gap, this study introduces a comprehensive novelty measurement that incorporates three types of relationships between knowledge units: network, semantic, and hierarchical. These relationships are used to quantify the latent distances among knowledge units. In this study, Mesh terms are used as proxies for knowledge units. Using a dataset of 142,036 articles published in PLoS ONE and a validation dataset from the H1 Connect platform, our results demonstrate that (1) each relationship type captures distinct latent distances between MeSH terms; (2) compared with the widely used indicators proposed by Uzzi et al. in 2013, our measures show stronger alignment with peer judgments; and (3) combining all three distance metrics yields more effective identification of novel papers than using any single perspective alone.
