Evaluating a GA-based approach to dynamic query approximation on an inference-enabled SPARQL endpoint

In this paper, we present an evaluation of our idea and its conceptual model on building endpoints having a mechanism to automatically reduce unwanted amount of inference computation by predicting its computational costs and allowing it to transform such a query into more speed optimized one by applying a GA-based query rewriting approach. Our preliminary evaluation shows the benefit on preventing unexpectedly long inference computations but keeping low variance of inference-enabled query executions by applying our query rewriting approach.