RDFox, the first market ready high-performance in-memory knowledge graph and semantic reasoner. Designed at the University of Oxford for performance and scalability.

High performance knowledge graph and semantic reasoning engine.

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Now we can do the impossible.”
- Dow Jones
Dow Jones, institutional financial company and user of RDFox.
RDFox 10x’ed the others.”
- Dow Jones
Dow Jones, institutional financial company and user of RDFox.
We went from a 3 day backload of our graph database to it taking 20 minutes.”
- Dow Jones
Dow Jones, institutional financial company and user of RDFox.
RDFox is allowing us to innovate where previously it wasn’t possible.”
- Dow Jones
Dow Jones, institutional financial company and user of RDFox.
RDFox has pushed the boundaries of performance.”
- Dow Jones
Dow Jones, institutional financial company and user of RDFox.
RDFox can do it quicker.”
- Dow Jones
Dow Jones, institutional financial company and user of RDFox.

The next generation of graph database

Drive your applications with data

Drive your applications with data

RDFox Secondary Image.

Bring the intelligence layer closer to your data

Add responsive pattern detection and verification to your applications

Add responsive pattern detection and verification to your applications

Scale your production environment and meet real-time demands

Scale your production environment and meet real-time demands

Reasonable Vehicles
Rule the Road

To get a driving license, humans learn the rules;
so should autonomous vehicles

RDFox for Autonomous Vehicles

White Paper 2020

Use Cases

Complex pattern detection

Complex pattern detection

Triples visualised

Knowledge graph

RDFox Secondary Image Feature

Semantic reasoning

Data integration

Data integration

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Who is the best Formula One driver of all time?

With so many variables in play, this is hardly an easy question to answer. But thankfully, complex questions are our specialty.

Go to f1.rdfox.tech to find the answer.

RDFox Features

The first market-ready knowledge graph designed from the ground up with reasoning in mind.

Efficiently manipulate data.

Fewer lines of code, quicker answers.

Save time by writing queries with rules.

Create data or alerts when patterns are found.

Materialise rules before querying to save time.

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RDFox the world's fastest and most powerful market-ready knowledge graph and semantic reasoning engine.
Machine Learning and Semantic Reasoning, the Perfect Union—Using Object Detection with RDFox

Machine Learning and Semantic Reasoning, the Perfect Union—Using Object Detection with RDFox

The two branches of AI, machine learning and semantic reasoning, are often discussed as opposing forces—one a probabilistic black-box and the other reliant upon provable logical inferences. In reality, their strengths can be combined to create a solution greater than the sum of its parts. While exploring RDFox, our in-house experts at Volvo Cars set out to do just that, pairing knowledge graph reasoning with object detection.

With this in mind, there were a few features of RDFox in particular that we wanted to explore:

·       OWL2 to build ontologies

·       Semantic reasoning for a real-time decision engine

·       In-memory data storage and management well suited for edge applications

We wanted to create a project utilizing these features to learn more about RDFox and to create something visually engaging to demonstrate the potential to our colleagues. So, after some brainstorming, we decided to try and feed the database with object detection information.

Detecting objects in an image using YOLOv5

Object detection is a growing field within computer vision related to identifying objects as well as their relative size and position. In this case, we used an open-source pre-trained model with 80 classes from the MS coco dataset as this suited our purposes for the project. These computer vision models can use various types of input data to extract features for classification, like point cloud data from LiDAR or RADAR. We opted for a trusty web camera to keep things simple. The model we used is the YOLOv5, an open-source PyTorch implementation and further developed version of the YOLO algorithm by Joseph Redmond. Like doctor Frankenstein, he became scared of his own creation and decided to halt his research when he saw its use in military applications and because of privacy concerns. He did an interesting TED talk about the potential benefits this technology has, as well as the dangers it unlocks. With this in mind, we decided to approach the project carefully... First adding the direct information that the model provides.

Object information fed to RDFox

The detected car and cup from the first figure are passed to RDFox together with a frame. Each object is also given a confidence, as well as information about their relative bounding boxes. If the bounding box of one object overlaps another, it :isOverlapping. If an object bounding box were to completely surround another, that object :isContaining the relatively smaller second object. Each frame is also time stamped and given a count integer from the start of the video stream. After that we decided to add a simple ontology to the graph to give the objects some basic classes. To build on this, one could add all sorts of wonderful meaning to the detected objects using the OWL2 web ontology language.

Objects with classes in RDFox

Our graph is growing. Next, we wanted to explore reasoning, so using rules we implemented a little made up scenario. If a vehicle of any kind is detected in the graph, we mark it with a heads up: 🙂. If there is more than one vehicle in the graph, we can guess that there is a bit more traffic, so we mark this with a somewhat more concerned alert: 😯. Finally, if bounding boxes are overlapping, we guess that this is an immediate traffic situation, like an overtake: 😱.

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Oxford Semantic Technologies is a spin out of the University of Oxford and is backed by leading investors including:

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High performance knowledge graph and semantic reasoning engine.