RDFox exploits a patented in-memory architecture and parallelised computation to provide high performance for data loading, reasoning and query answering. Key features of RDFox include:
RDFox addresses the above challenges by using novel algorithms and data structures developed at Oxford. These support (mostly) lock-free updates, allowing for highly parallelized computation of the materialization, while at the same time are compact, allowing for the storage of large knowledge graphs. The algorithms and data structures also support incremental reasoning for fast updates when the data changes.
RDFox has been tested on a wide range of hardware and data sets and has proved to be both robust and highly scalable. We have conducted tests on Numa-0-0, which has 67 nodes (each node has 6 processing units), which relative distances between that go from 10 (intra-node distance) to 200. The tests used the Claros dataset, which is particularly interesting as the materialisation increases the size of the graph more than 20 times. The performance depends on node distances, but when bound to near nodes is comparable to the SPARC T5-8 which we used in earlier tests (see AAAI paper).