  The Semantic Web is built on top of Knowledge Organization Systems (KOS)
(vocabularies, ontologies, concept schemes) that provide a structured,
interoperable and distributed access to Linked Data on the Web. The maintenance
of these KOS over time has produced a number of KOS version chains: subsequent
unique version identifiers to unique states of a KOS. However, the release of
new KOS versions pose challenges to both KOS publishers and users. For
publishers, updating a KOS is a knowledge intensive task that requires a lot of
manual effort, often implying deep deliberation on the set of changes to
introduce. For users that link their datasets to these KOS, a new version
compromises the validity of their links, often creating ramifications. In this
paper we describe a method to automatically detect which parts of a Web KOS are
likely to change in a next version, using supervised learning on past versions
in the KOS version chain. We use a set of ontology change features to model and
predict change in arbitrary Web KOS. We apply our method on 139 varied datasets
systematically retrieved from the Semantic Web, obtaining robust results at
correctly predicting change. To illustrate the accuracy, genericity and domain
independence of the method, we study the relationship between its effectiveness
and several characterizations of the evaluated datasets, finding that
predictors like the number of versions in a chain and their release frequency
have a fundamental impact in predictability of change in Web KOS. Consequently,
we argue for adopting a release early, release often philosophy in Web KOS
development cycles.
