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RDFox is an optimised RDF triple store and parallel Datalog reasoning engine developed at the University of Oxford. OST’s development team has applied state of the art techniques and deep expertise in computer science to exploit the full power of modern multi-core architecture.

RDFox scales extremely well with every additional processor core and brings, for the first time, mid-sized and large applications of Semantic Technologies into the reach of commodity hardware.

Datalog is a well understood rule language which allows for neat and elegant formulation of queries and views. With Datalog, one can encode specifications, regulations and policies given in natural language whilst maintaining a high correspondence to the original text. Datalog's clarity shortens development cycles and enormously increases the maintainability of implemented projects.

Developing solutions in Datalog requires less IT-expert skill and allows domain experts to formulate queries by themselves. Development cycles for data analysis tasks are drastically reduced. Datalog explanations for query answers show exactly how a certain answer was derived from the given data. This allows the domain expert to validate developed solutions and gives certainty about the obtained results.



SPARQL query answering

Datalog and SWRL rules

OWL 2 RL ontologies

Extensions to Datalog

Existential rules
e.g. SKOLEM, Negation as failure

Incremental reasoning



Scalable and robust performance

Parallel reasoning

Highly optimised data structure via lock-free parallel inserts

Main memory, multi core architecture


Supported on Windows, MacOS, Linux


Ready to deploy as a Docker image

Tested for Kubernetes

SSL security