MeSH Concept Relevance and Knowledge Evolution: A Data-driven Perspective
Jenny Copara, Nona Naderi, Gilles Falquet, Douglas Teodoro
TL;DR
This study introduces a data-driven framework to quantify the evolving relevance of MeSH concepts by integrating PubMed annotations, the MeSH hierarchy, and the PubMed citation network. It defines four relevance aspects—informativeness via $H(X)$, usefulness via category utility $CU$, disruptiveness via a disruption index $D$, and influence via PageRank centrality—then propagates and fuses these scores with Reciprocal Rank Fusion to obtain a unified ranking. The approach is validated on MeSH terminology evolution and retracted papers, showing that evolving concepts have higher mean relevance ($2.09\times10^{-3}$) than unchanged ones ($8.46\times10^{-4}$) and that concepts in retracted papers differ from those in non-retracted ones (roughly $0.17$ vs $0.15$). Limitations include sampling the citation network due to computational complexity, and the authors propose extending the framework to other domains and improving scalability in future work.
Abstract
The Medical Subject Headings (MeSH), one of the main knowledge organization systems in the biomedical domain, continuously evolves to reflect the latest scientific discoveries in health and life sciences. Previous research has focused on quantifying information in MeSH primarily through its hierarchical structure. In this work, we propose a data-driven approach based on information theory and network analysis to quantify the relevance of MeSH concepts. Our method leverages article annotations and their citation networks to compute four aspects of relevance -- informativeness, usefulness, disruptiveness, and influence -- over time. Using both the citation network and the MeSH hierarchy, we compute these relevance aspects and apply an aggregation algorithm to propagate scores to parent nodes. We evaluated our approach on MeSH terminology changes and showed that it effectively captures the evolution of concepts. The mean relevance of evolving concepts is higher compared to concepts that remained unchanged ($2.09E-03$ vs. $8.46E-04$). Moreover, we validated the framework by analyzing retracted articles and found that concepts used to annotate retracted articles (mean relevance: 0.17) differ substantially from those annotating non-retracted ones (mean relevance: 0.15). Overall, the proposed framework provides an effective method for ranking concept relevance and can support the maintenance of evolving knowledge organization systems.