--- title: RDFox Reviews meta_title: 'RDFox Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 14 reviews by the users' company size, role or industry to find out how RDFox works for a business like yours. aggregate_rating: rating_value: 4.8 review_count: 14 scale: '5' date_modified: '2026-06-04' parent_category: name: NoSQL Databases url: https://www.g2.com/categories/nosql-databases --- # RDFox Reviews **Vendor:** Oxford Semantic Technologies **Category:** [RDF Databases](https://www.g2.com/categories/rdf-databases) **Average Rating:** 4.8/5.0 **Total Reviews:** 14 ## About RDFox 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 Reviews ### 1. RDFox has some interesting and unique features! **Rating:** 4.5/5.0 stars **Reviewed by:** Susanne C. | Studentin, Enterprise (> 1000 emp.) **Reviewed Date:** June 15, 2023 **What do you like best about RDFox?** 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. **What do you dislike about RDFox?** The documentation is quite technical and could include more examples and provide more background on some RDFox features. **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. ### 2. Makes SPARQL and RDF/linked data a joy to work with! **Rating:** 5.0/5.0 stars **Reviewed by:** Timothy T. | Librarian for Applied Metadata Research, Enterprise (> 1000 emp.) **Reviewed Date:** May 24, 2023 **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. **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. **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. ### 3. Performant graph database with great features and a responsive and helpful vendor **Rating:** 5.0/5.0 stars **Reviewed by:** Padraig A. | Lead Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 15, 2023 **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 **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. **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 ### 4. Powerful database with great support **Rating:** 4.5/5.0 stars **Reviewed by:** Luis Angel M. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** February 08, 2023 **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. **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. **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. ### 5. FLEXIBLE, FAST RDF STORE AND REASONING ENGINE - GREAT SUPPORT FROM THE COMPANY **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Retail | Mid-Market (51-1000 emp.) **Reviewed Date:** November 02, 2022 **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. **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). **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. ### 6. Low barrier to entry with minimal impact on infrastructure. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User **Reviewed Date:** January 20, 2023 **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 **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. **What problems is RDFox solving and how is that benefiting you?** Data classification, and ultimately, federating different data across the organization. ### 7. RDFox is incredibly fast! **Rating:** 5.0/5.0 stars **Reviewed by:** Marcelo B. | Knowledge Graph Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** October 18, 2021 **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. **What do you dislike about RDFox?** I haven't come across any downsides from a performance point of view. **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. **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. ### 8. Fast Database With Great Reasoning Capabilities **Rating:** 5.0/5.0 stars **Reviewed by:** Marcus N. | Division Cost and Value Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** October 14, 2021 **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. **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. **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. ### 9. RDFox is powerful and very fast **Rating:** 5.0/5.0 stars **Reviewed by:** Nicolas R. | Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** October 08, 2021 **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 ! **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 **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. ### 10. A very intuitive piece of software **Rating:** 4.5/5.0 stars **Reviewed by:** Vaishali R. | Knowledge Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 23, 2021 **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! **What do you dislike about RDFox?** Nothing that I can think of, I really enjoy using RDFox. **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. ### 11. Screaming fast triple store enabling real-time use cases **Rating:** 5.0/5.0 stars **Reviewed by:** Jacobus G. | CEO, Small-Business (50 or fewer emp.) **Reviewed Date:** October 11, 2021 **What do you like best about RDFox?** 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. **What do you dislike about RDFox?** No support for clustering yet. I believe that feature is coming soon though. **What problems is RDFox solving and how is that benefiting you?** Using it for advanced model-driven data ingestion pipelines where RDFox's radically faster loading speed makes all the difference. ### 12. Rules for everything **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Automotive | Mid-Market (51-1000 emp.) **Reviewed Date:** January 25, 2022 **What do you like best about RDFox?** I like the following features of RDFox: 1. server- datastore archtiecutre 2. multi-threading and sophisticated indexing 3. flexible rule language with negation and aggregation 4. fast incremental updates 5. explanation of inferences 6. monitoring rule evalaution process **What do you dislike about RDFox?** Some further improvements: 1. rule mangement: rule editor, dependency graph, 2. rule2sparql translation (at least for basic structure), which will help to debug the rules using SPARQL queries. 3. datastore priority configation 4. SPARQL console can return the total number of rows. Supporting the shortcut key (crl+/) for commenting on the SPARQL queries. **Recommendations to others considering RDFox:** if you want to use in a resource-constrained environment for fast prototyping, please consider RDFox **What problems is RDFox solving and how is that benefiting you?** I am solving data process and integration problems. The multi-datastores supported by RDFox could process the data in parallel and integrate the produced result from each datastore into one graph. ### 13. RDFox is Brilliant - I have used it in research and industry. **Rating:** 5.0/5.0 stars **Reviewed by:** Angus A. | PhD Researcher, Small-Business (50 or fewer emp.) **Reviewed Date:** September 24, 2021 **What do you like best about RDFox?** I love the incremental reasoning engine. Aside from this - I like that I can create a start script to automate initialisation, and the visualisation studio. I have loaded some fairly large datasets and RDFox remains very fast. **What do you dislike about RDFox?** The documentation has been unclear at times. However, my questions have always been answered quickly (and the documentation updated after that). **What problems is RDFox solving and how is that benefiting you?** When I first started using RDFox, I was wanting to quickly query over a graph that grew over time. I am now working on a research project that requires more than just querying - I am reasoning over the graph as it grows in real-time. RDFox allows me to do this very efficiently, and I can tweak this reasoning layer in a controlled manner. ### 14. High performance deductive database in industry scenarios **Rating:** 5.0/5.0 stars **Reviewed by:** Robert D. | CTO, Mid-Market (51-1000 emp.) **Reviewed Date:** October 08, 2021 **What do you like best about RDFox?** High-performance SPARQL querying and Datalog reasoning even with large datasets. In-store SHACL engine that is easy to use. We integrate our software platform and develop real-time recommender use cases, which are made possible because of the fast incremental reasoning. **What do you dislike about RDFox?** I would apprechiate SPARQL update for the Web UI. **What problems is RDFox solving and how is that benefiting you?** We run our software platform on top of triple stores like RDFox, and the features benefit from the high performance. We also implement real-time knowledge-based recommendation use cases using the Datalog engine to infer relations and similarity scores automatically. ## RDFox Discussions - [What is RDFox used for?](https://www.g2.com/discussions/what-is-rdfox-used-for) - [View RDFox pricing details and edition comparison](https://www.g2.com/products/rdfox/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-26+11%3A24%3A16+-0500&secure%5Bsession_id%5D=3a91087b-abe5-4ab6-809a-79d6b9fa9a3c&secure%5Btoken%5D=9b1184ed14b2848a3df7551ac547b90fd4836c0f10c3df88d6095917cd9eb8f9&format=llm_user) ## RDFox Features **Data Management** - Data Model - Data Types - Built - In Search - Query Language **Availability** - Auto Recovery **Performance** - Query Optimization **Security** - Role-Based Authorization - Audit Logs **Support** - BI Connectors - Operating Systems **Database Features** - Storage - Availability - Stability - Scalability - Security - Data Manipulation - Query Language ## Top RDFox Alternatives - [Graphwise GraphDB](https://www.g2.com/products/graphwise-graphdb/reviews) - 4.2/5.0 (6 reviews) - [Neo4j Graph Database](https://www.g2.com/products/neo4j-graph-database/reviews) - 4.5/5.0 (131 reviews) - [Stardog](https://www.g2.com/products/stardog/reviews) - 4.2/5.0 (17 reviews)