Enhancing Rule-based OWL Reasoning on Spark

The rule-based OWL reasoning is to compute the deductive closure of an ontology by applying RDF/RDFS and OWL entailment rules. In previous work, we present an approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy. However, some key optimizations that based on LUBM do not generalize to more diverse datasets. In this paper, we analyze these problems, and demonstrate the inference engine. We have evaluated the approach using the real-world datasets. The experimental results show that our approach also achieve better performance as compared to Kim & Park’s algorithm (2015).