We address the problem of estimating when the results of an input SPARQL query
over dynamic RDF datasets will change. We evaluate a framework that extracts
features from the query and/or from past versions of the target dataset and
inputs them into binary classifiers to predict whether or not the results for a
query will change at a fixed point in the near future. For this evaluation, we
create a gold standard based on 23 versions of Wikidata and a curated
collection of 221 SPARQL queries. Our results show that the quality of
predictions possible using (only) features based on the query structure and
lightweight statistics of the predicate dynamics - though capable of beating a
random baseline - are not competitive with results obtained using (more costly
to derive) knowledge of the complete historical changes in the query results.