RDFox Reviews & Product Details

The instant execution of Datalog rules is one of the best features, as well as the reasoning capabilities, which have some useful and unique features. RDfox is very good for dynamic and passing data. RDFox has a very fast reaosning, it is very convenient to write scripts to save time when working with RDFox. Review collected by and hosted on G2.com.

The documentation is quite technical and could include more examples and provide more background on some RDFox features. Review collected by and hosted on G2.com.

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.

- straightforward to get up-and-running

- rules based semantic reasoning is a real superpower compared to some other graph databases

- multiple deployment options, including high-availability pattern

- built-in connector to Apache Solr makes building search applications highly tractable

- great support and documentation Review collected by and hosted on G2.com.

Not really a problem with RDFox, but SPARQL and TTL can take a while to get your head around when you're starting out. Review collected by and hosted on G2.com.

It has a low entry barrier, and the learning curve is reasonable. Great support from the RDFox team! They helped us greatly on integrate it with the current architecture, and to keep giving maintenance as new versions got released. Review collected by and hosted on G2.com.

The project faced some concurrency issues, but overall, performance is good. At one time at the project we stopped upgrading the database version, and that's why I don't feel able to review the latest version. Review collected by and hosted on G2.com.

1. Clear and complete documentation https://docs.oxfordsemantic.tech/introduction.html .

2. Rich set of commands and options to customize solutions and attack problems efficiently.

3. Support for datalog that allows one to customize inference rules.

4. Multiple datastores and named graphs.

5. Efficiently implemented incremental and revisable reasoning.

6. Endpoint to work with datastores in Python (e.g. can perform sparql queries and export triples using Python).

7. Reasoning on axioms (importaxioms) as distinct from additional inference rules (TBox datalog file).

8. Command line interface commands and scripts.

9. Can be implemented in different places (local machine or cloud) allowing customization of available RAM etc.

10. Easy to provide feedback.

11. Extension of SPARQL with new functions amd support for RDF-star and SPARQL-star.

12. GREAT CUSTOMER SUPPORT. Review collected by and hosted on G2.com.

If you do not like something, they will take your feedback seriously and try to meet your needs in a next release. Currently, I do not like the following (mostly minor) things:

1. The SPARQL implementation does not include DESCRIBE.

2. The browser does not show the cardinality of the results (how many results did a query get?).

3. There is no autocompletion for user-created strings.

4. RDFox does not have a specific function to check consistency and satisfiability (contrast this with Protege's reasoner and Protege's Debugger plugin).

5. RDFox does not have a keyboard shortcut to comment out a line.

6. RDFox does not have a dlog file to isert the subclass relations of csd types.

7. Their TBox dlog file could be more complete concerning triples involving owl:Thing.

8. The browser does not allow to duplicate pages when the SPARQL query is long.

9. They do not extend SPARQL to include function to carry out graph analysis (e.g. shortest path). Review collected by and hosted on G2.com.

Performance! I tested RDFox with the LUBM Benchmark and the timings recorded to load and query data were quite impressive. Review collected by and hosted on G2.com.

I haven't come across any downsides from a performance point of view. Review collected by and hosted on G2.com.

Easy setup with Docker images in the Cloud, but also implementation on a local machine is very straightforward.

Oxford Semantic provides excellent personal introduction sessions to bring you up to speed and start using the database.

Datalog implementation is beneficial in overcoming OWL limitations (missing data, constraint checking). After some getting used to it, the data log rules are quite straightforward to apply. Also, if one gets stuck, the support by Oxford Semantics is very responsive (mostly within a business day). The help provided is beyond technical tool support as it also offers suggestions on how to solve specific problems with your rule sets or ontology structure.

As far as I can say, the reasoning (processing new rule sets) is fast. Incremental reasoning (if new data is added) is also a great feature if you deal with massive data sets being updated regularly.

Not less important: Oxford Semantics has a friendly team that makes it fun to interact with. Review collected by and hosted on G2.com.

The documentation could be a bit better. However, it was also possible to quickly get clarifications from the Support.

Adding tutorial videos might be helpful. Review collected by and hosted on G2.com.

I am actually working for a research project in the medical field (CDSS) in partnership with the industry. RDFox has been chosen and I use it all the time. I am always impressed by its power and speed of execution. For example, a request time of several minutes with other SPARQL engines is often solved in less than one second with RDFox ! Review collected by and hosted on G2.com.

Perhaps more functionality in console web site, but RDFox team is very reactive, and each new release brings many improvements.

Console web, in 5.2 release, becomes now very pretty with SPARQL syntax color highlighting and syntax completion. And also a fantastic web tool for dynamically creating graphs based on the triples of a query Review collected by and hosted on G2.com.

It's very straightforward and intuitive to use; it's great for beginners like myself and has allowed me to pick up a lot very quickly. It has also sped up a lot of functionality in our product, which is all the more desirable for Legislate! Review collected by and hosted on G2.com.

Nothing that I can think of, I really enjoy using RDFox. Review collected by and hosted on G2.com.

RDFox is a "lean and mean" semantic graph database, Oxford Semantic Technologies clearly understands what the key priorities are for anyone who wants to deploy a database in production: it has to be fast, reliable and predictable. Many of their competitors focus on adding more and more (often proprietary) features forgetting (a bit) about these three key priorities.

RDFox has a radically different design than other semantic graph databases in the sense that it is an "in-memory" database (with full ACID transaction support) which means that it is screaming fast (many LUBM queries are more than a 1000 times faster than the nearest competitor) but it also means that it really needs a lot of memory for large datasets.

Fortunately, if you'd be building an Enterprise Knowledge Graph (EKG) according to the 10 principles of the EKGF (see https://ekgf.org/principles) you would not need to have all your datasets in just one database instance and could freely scale horizontally. RDFox would allow you to support real-time use cases like pre-trade risk calculation or other advanced use cases using many complex datasets. Review collected by and hosted on G2.com.

No support for clustering yet. I believe that feature is coming soon though. Review collected by and hosted on G2.com.