High quality

Keep an overview of how you integrate your data in a knowledge graph, and keep it simple to improve your process. The declarative configuration document is a single place to inspect how your data should be integrated.
Normalization functions can be integrated to improve the quality of your data values, and the semantic integrity of you knowledge can already be validated, even before any data is generated.

For more information on the declarative configuration, see the introduction on RML.
For more information on the data transformations, see the connection between FnO and RML
For more information on data validation, see

High speed

Big or small, generate your knowledge using the right type of software. The RMLMapper is our reference implementation in JAVA with full functionality, perfect for smaller datasets. The RMLStreamer is our streaming solution in SCALA built on KAFKA and Flink, capable of scaling up as much as you need.

Intuitive user interface

Whether you like a Graphical User Interface, a developer-friendly notation, or hardcore detailed semantic descriptions, we got you covered. Depending on your background, you can have a look at the RMLEditor, Matey, or the RML spec.

Easy deployment

Our processors are MIT licensed Open Source projects which you can find on Github. Deployment is eased by providing Docker files. If you would like to collaborate on deployed solutions, feel free to contact us.