As an in-memory solution, RDFox can ingest RDF data with blazing speed. In practice, with a dataset that occupied 167GB of RAM, ingest took 18 minutes when parallelized. The system is straightforward to set up and configure.

The RDFox implementation of Datalog rules makes it possible to answer "impossible" queries. Our team had a complex query that initially took 38 minutes to run. After we added rules to simplify our data patterns, query evaluation dropped to 10 milliseconds!

With rules, semantic "views" can be precomputed on the data side. This ability can simplify the creation and composition of entity-driven user interfaces, speeding up the front-end development process.

RDFox provides connectors to external data sources such as Solr, enabling powerful integration with full-text search.

The team at Oxford Semantic Technologies is top-notch, with strong academic credentials: RDFox represents the best in research-driven product development. The product is constantly improving, with recent enhancements focused on high availability and robust support for named graphs. Overall, RDFox technical support was outstanding, and any issues were promptly addressed. Review collected by and hosted on G2.com.

The documentation for RDFox is quite thorough, but it could benefit, in places, from additional examples of concrete usage (such as the actual commands required in the RDFox shell).

Support for additional RDF serializations, such as JSON-LD, would be nice to have. Support for SPARQL 1.1 federated queries with the SERVICE keyword would also be helpful. Review collected by and hosted on G2.com.