Estimating the Dynamics of SPARQL Query Results Using Binary Classification

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.