RDFox

A highly performant engine that builds and manages knowledge graphs, enabling semantic reasoning.

Mirrors human reasoning principles, inferring new knowledge exclusively from factual data, ensuring accuracy, truth, and explainability for AI applications.

Dynamically applies consequences of rules to the database in real-time as data is added, changed, or removed, without needing a restart or re-analyzing the entire dataset.

Automatically materializes (stores) inferred knowledge and data directly into the knowledge graph, eliminating the need for manual updates via queries.

Allows inference of new knowledge based on data that does not exist in the knowledge graph, providing more powerful rules-based AI capabilities for handling absent knowledge.

Performs numeric value calculations across groups of data in the knowledge graph, automatically materializing these as new knowledge.

Specifically designed to run data in RAM, leveraging faster data transfer speeds (10-1000x faster than SSDs) for rapid loading and query times (milliseconds for billions of triples).

Consistently outperforms competitors by orders of magnitude in benchmark tests, processing 2-3 million inferences of new facts per second.

Built to handle massive knowledge bases and datasets with billions of deeply interconnected facts, as well as tiny on-device applications.

Supports replication of knowledge and data changes across multiple running RDFox servers to ensure uninterrupted service in case of an outage.

Can be deployed on cloud instances to manage large datasets.

Can be run smoothly on-premises for development or convenience.

Designed for greater security, mobility, and in-situ analytics, installable on edge and mobile devices.

Technology for driving the next generation of the Internet of Things (IoT) on edge devices.

Features a highly optimized memory footprint, allowing deployment on resource-constrained devices like Raspberry Pi or microcontrollers.

Provides access to their expert knowledge engineers as standard, supporting projects.

Uses an inbuilt graph database based on the open RDF standard, offering flexibility and reducing vendor lock-in.

Supports SPARQL 1.1 for querying the knowledge graph.

Uses Datalog and SWRL for defining reasoning rules.

Supports OWL 2 (RL) for defining ontologies.

Used for validating data against defined shapes.

Capable of managing extremely large datasets while maintaining performance.

Accessible via industry-standard RESTful or Java Application Programming Interfaces.

Supports various data formats for import and export, including CSV, JSON, N-Triples, Turtle, TriG, and OWL 2 Functional-Style Syntax.

Delivers accurate search results and relevant recommendations.

Provides instant decisions and detection based on complex rules and patterns for regulated industries (e.g., financial services, healthcare) to combat fraud and ensure compliance.

Delivers immediate and optimal solutions for matching compatible product configurations in real-time for manufacturers and retailers.

Provides the "brain" for autonomous systems (cars, trucks, aircraft, boats) to make correct and immediate decisions, offering explainable and accurate platforms.

Enables rapid data integration and advanced analysis to find valuable knowledge in large datasets.