  The Medical Subject Headings (MeSH), one of the main knowledge organization
systems in the biomedical domain, is constantly evolving following the latest
scientific discoveries in health and life sciences. Previous research focused
on quantifying information in MeSH using its hierarchical structure. In this
work, we propose a data-driven approach based on information theory and network
analyses to quantify the knowledge evolution in MeSH and the relevance of its
individual concepts. Our approach leverages article annotations and their
citation networks to compute the level of informativeness, usefulness,
disruptiveness, and influence of MeSH concepts over time. The citation network
includes the instances of MeSH concepts or MeSH headings, and the concept
relevance is calculated individually. Then, this computation is propagated to
the hierarchy to establish the relevance of a concept. We quantitatively
evaluated our approach using changes in the MeSH terminology and showed that it
effectively captures the evolution of the terminology. Moreover, we validated
the ability of our framework to characterize retracted articles and show that
concepts used to annotate retracted articles differ substantially from those
used to annotate non-retracted. The proposed framework provides an effective
method to rank concept relevance and can be useful in maintaining evolving
knowledge organization systems.
