  Vocabularies are used for modeling data in Knowledge Graphs (KG) like the
Linked Open Data Cloud and Wikidata. During their lifetime, the vocabularies of
the KGs are subject to changes. New terms are coined, while existing terms are
modified or declared as deprecated. We first quantify the amount and frequency
of changes in vocabularies. Subsequently, we investigate to which extend and
when the changes are adopted in the evolution of the KGs. We conduct our
experiments on three large-scale KGs for which time-stamped snapshots are
available, namely the Billion Triples Challenge datasets, Dynamic Linked Data
Observatory dataset, and Wikidata. Our results show that the change frequency
of terms is rather low, but can have high impact when adopted on a large amount
of distributed graph data on the web. Furthermore, not all coined terms are
used and most of the deprecated terms are still used by data publishers. There
are variations in the adoption time of terms coming from different vocabularies
ranging from very fast (few days) to very slow (few years). Surprisingly, there
are also adoptions we could observe even before the vocabulary changes are
published. Understanding this adoption is important, since otherwise it may
lead to wrong assumptions about the modeling status of data published on the
web and may result in difficulties when querying the data from distributed
sources.
