发表机构
Anhui University; Central China Normal University; Nanjing University of Science and Technology（安徽大学; 华中师范大学; 南京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出纳入网络、语义、层级三类知识单元关系的新颖性测量方法，利用14.2万篇PLoS ONE论文及H1 Connect验证集，发现其与同行判断一致性更强，结合三类指标可更有效识别新颖论文。
AI 中文摘要
衡量科学论文的新颖性是研究评估与科学计量学的核心问题。从重组视角出发，现有研究大多聚焦于知识单元的共现关系来评估论文新颖性，但常忽略知识单元间的其他关系，这种狭隘视角可能导致新颖性评估不准确或不完整。为填补这一空白，本研究提出一种综合新颖性测量方法，纳入知识单元间的三类关系：网络关系、语义关系与层级关系，用以量化知识单元间的潜在距离。研究使用PLoS ONE期刊发表的142036篇文章组成的数据集，以及H1 Connect平台的验证数据集，结果表明：（1）每类关系都能捕捉到MeSH术语间不同的潜在距离；（2）与Uzzi等人（2013）提出的广泛使用的指标相比，本研究的测量方法与同行判断的一致性更强；（3）结合全部三类距离指标，比单独使用任一视角更能有效识别新颖论文。
英文摘要
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.
Journal refJournal of Information Science, 2026
