On an inference-enabled Linked Open Data(LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. In optimizing an OWL ontology for executing SPARQL queries on an LOD endpoint with inference engine, there are a number of SPARQL queries that we would probably take into account and many of these factors possibly work against each other. In this paper, for reducing query execution time on an inference-enabled LOD

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... oint, we propose an idea and its implementation to employ a constrained multi-objective evolutionary approach to make modification of ontologies based on the past-processed queries and their results. We employ a multi-objective evolutionary approach to realize better promoting and preserving diversity within the population. We also employ constraint handling to dealing with invalid solution such as inconsistent ontology. We show how the approach works well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.