RDFox is a high-performance in-memory knowledge graph and semantic reasoner. Optimised for speed and advanced reasoning, it affords query and loading times that are orders of magnitudes faster than alternative triplestores, while also achieving greater insights into the data. RDFox is developed by Oxford Semantic Technologies—an Oxford University spin-out founded by leading academics backed by decades of cutting-edge research in semantic web technologies.
RDFox is the high-performance in-memory knowledge graph and semantic reasoner. Designed by leading academics at the University of Oxford, RDFox is the child of groundbreaking research that has culminated in the fasted market-ready knowledge graph, owing to its optimised in-memory approach. Coupled with its unmatched reasoning capabilities, it is the high-end competitor to the industry standards. Supporting Datalog, OWL 2 RL, SPARQL, and validation with SHACL, RDFox complies with W3C standards, allowing easier adoption.
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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.
What do you dislike about RDFox?
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.
What problems is RDFox solving and how is that benefiting you?
In particular, the way of incremental reasoning, the Datalog Rules with their immediate execution as soon as new data arrives from different sources. It is possible to push data into RDFox via different APIs. In the web frontend, you can also see what conclusions the reasoning engine has drawn, and you can run SPARQL-queries to retrieve triples.
I used RDFox to track real weather data from https://sensor.community/en/ every 5 minutes and do retrospetive analysis. Review collected by and hosted on G2.com.
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TT
Timothy T.
Librarian for Applied Metadata Research
Enterprise (> 1000 emp.)
"Makes SPARQL and RDF/linked data a joy to work with!"
What do you like best about RDFox?
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.
What do you dislike about RDFox?
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.
What problems is RDFox solving and how is that benefiting you?
Mapping from multiple data sources to a common ontology to enable data integration, powered by an enterprise knowledge graph. Review collected by and hosted on G2.com.
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PA
Padraig A.
Lead Data Scientist
Mid-Market (51-1000 emp.)
"Performant graph database with great features and a responsive and helpful vendor"
What do you like best about RDFox?
- 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.
What do you dislike about RDFox?
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.
What problems is RDFox solving and how is that benefiting you?
We were able to use RDFox to build a few different user-facing applications, including
- personalised recommendations for our users, based on a graph version of collaborative filtering with additional rules
- a faceted search interface that used RDFox + Apache Solr to return filterable results Review collected by and hosted on G2.com.
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LM
Luis Angel M.
Data Engineer
Small-Business (50 or fewer emp.)
"Powerful database with great support"
What do you like best about RDFox?
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.
What do you dislike about RDFox?
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.
What problems is RDFox solving and how is that benefiting you?
Setting up a Knowledge Graph, data classification. Friendly integration with the current tech stack, which is heavily based on Scala/Java software, as well with the design of current data pipelines. Review collected by and hosted on G2.com.
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UR
Verified User in Retail
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name, job title, or picture.
Mid-Market (51-1000 emp.)
"FLEXIBLE, FAST RDF STORE AND REASONING ENGINE - GREAT SUPPORT FROM THE COMPANY"
What do you like best about RDFox?
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.
What do you dislike about RDFox?
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.
What problems is RDFox solving and how is that benefiting you?
RDFox solves the problem of finding a scalable, fast, reliable RDF store with (almost) full SPARQL capabilities and efficient reasoning. It makes creating and maintaining the ontology faster (hours as opposed to days) than simply using Python and Protege. It is possible to conduct analyses of the ontology (even unplanned requests) precisely and timely. It integrates with several other projects, including the integration of RDFox's semantic capabilities with ML. Review collected by and hosted on G2.com.
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name, job title, or picture.
"Low barrier to entry with minimal impact on infrastructure."
What do you like best about RDFox?
* Low barrier to entry; graph visualizations; easy data upload; in-memory persistence.
* Responsive support team
* Enterprise features: HA, transactions
* Performance Review collected by and hosted on G2.com.
What do you dislike about RDFox?
No dislikes. In fact, I found a minor bug during our evaluation, and the engineering team had a fix the next day. Review collected by and hosted on G2.com.
What problems is RDFox solving and how is that benefiting you?
Data classification, and ultimately, federating different data across the organization. Review collected by and hosted on G2.com.
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MB
Marcelo B.
Knowledge Graph Engineer
Small-Business (50 or fewer emp.)
"RDFox is incredibly fast!"
What do you like best about RDFox?
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.
What do you dislike about RDFox?
I haven't come across any downsides from a performance point of view. Review collected by and hosted on G2.com.
Recommendations to others considering RDFox:
Performance is what any database should aim for as the number one priority and RDFox seems to take it very seriously. Review collected by and hosted on G2.com.
What problems is RDFox solving and how is that benefiting you?
I have been working on the optimisation of SPARQL queries in DataOps Pipelines and Transactional RDF Knowledge Graphs as well as executing and extending industry standard benchmarks. I was quite impressed with the performance of the reasoning materialisation on RDFox and the flexibility of its Datalog Inference Rules. Review collected by and hosted on G2.com.
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MN
Marcus N.
Division Cost and Value Engineer
Enterprise (> 1000 emp.)
"Fast Database With Great Reasoning Capabilities"
What do you like best about RDFox?
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.
What do you dislike about RDFox?
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.
What problems is RDFox solving and how is that benefiting you?
Reasoning over massive data sets which get updated on a regular basis.
It provides constraint checking on business data, automatic categorization, and tagging.
Inferencing new relations supports data integration of various data sources. Incremental reasoning enables building continuous stable data pipelines. Review collected by and hosted on G2.com.
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NR
Nicolas R.
Software Engineer
Enterprise (> 1000 emp.)
"RDFox is powerful and very fast"
What do you like best about RDFox?
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.
What do you dislike about RDFox?
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.
What problems is RDFox solving and how is that benefiting you?
Request which need to solve for a set of medical signs, the disorder associated. This is complex request and time answer need to be fast. Review collected by and hosted on G2.com.
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VR
Vaishali R.
Knowledge Engineer
Small-Business (50 or fewer emp.)
"A very intuitive piece of software"
What do you like best about RDFox?
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.
What do you dislike about RDFox?
Nothing that I can think of, I really enjoy using RDFox. Review collected by and hosted on G2.com.
What problems is RDFox solving and how is that benefiting you?
No problems as of yet, Legislate is still in the early stages of implementing and using RDFox to its full effect. We have acknowledged how quick RDFox is at processing and returning results which is one of its key benefits. We also like data manipulation in RDFox when provided with rules and logic on how to process the data. Review collected by and hosted on G2.com.
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