# Measuring the Novelty of Biomedical Papers Using the Latent Distances between Knowledge Units

arXiv: https://arxiv.org/abs/2609.05175 · HTML: https://mlsanity.com/paper/2609.05175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 69 references that are in the map.
- First posted on arXiv 4 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [A review on the novelty measurements of academic papers](https://mlsanity.com/paper/2501.17456/) (2025)
- [Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data](https://mlsanity.com/paper/1910.03233/) (2019)
- [Analyzing Linguistic Complexity and Scientific Impact](https://mlsanity.com/paper/1907.11843/) (2019)
- [Pandemics are catalysts of scientific novelty: Evidence from COVID-19](https://mlsanity.com/paper/2009.12500/) (2020)
- [Evaluating the quality of published medical research with ChatGPT](https://mlsanity.com/paper/2411.01952/) (2024)
- [Do more heads imply better performance? An empirical study of team thought leaders' impact on scientific team performance](https://mlsanity.com/paper/2606.26483/) (2026)

## Abstract (arXiv)

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. 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 to the widely used indicators proposed by Uzzi et al. (2013), our measures show stronger alignment with peer judgements; and (3) combining all three distance metrics yields more effective identification of novel papers than using any single perspective alone.