Parallel OWL 2 RL materialisation in centralised, main-memory RDF systems

We present a novel approach to parallel materialisation (i.e., fixpoint computation) of OWL RL Knowledge Bases in centralised, main-memory, multi-core RDF systems. Our approach comprises a datalog reasoning algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, ‘mostly’ lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well so, with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.