RML is an extension of R2RML. RML rules are executed by processors. To ease the creation and execution of these rules, we developed graphical user interfaces and YARRRML, a human-readable text-based representation based on YAML, together with the relevant tooling. We created wrappers to easy the use of our processors in your development environment. It is also possible to validate your RML rules to improve the quality of your resulting knowledge graphs.
An RML processor executes RML rules and generate the corresponding knowledge graphs.
The RMLMapper executes RML rules to generate high-quality Linked Data from multiple originally (semi-)structured data sources.
→ GithubThe RMLStreamer executes RML rules to generate high-quality Linked Data from multiple originally (semi-)structured data sources in a streaming way.
→ GithubGraphical user interfaces make it easier for users to work with RML rules and the resulting knowledge graphs.
The RMLEditor offers a Graphical User Interface to enable data publishers, who are domain experts, to model knowledge derived from heterogeneous distributed data.
→ Try it out! - Github - Demo (video) - Learn moreA Web application that helps users with managing and executing their RML rules.
→ GithubA visual editor for RDF constraints currently supporting the visual notations ShapeUML and ShapeVOWL and import/export/validation of SHACL constraints.
→ GithubWrappers make it easier for developers to work with our processors in their development environment.
Easily download the jar of a specific or latest version of the RMLMapper, either via the command line interface or directly from within your application.
→ GithubYARRRML is a human-friendly text-based representation of RML rules.
Matey is a browser-based application that helps you write YARRRML rules. The corresponding RML rules can be exported for use outside of Matey. Additionally, the rules can be executed on a sample of the data, which allows users to inspect the generated Linked Data.
→ Try it out! - Demo (video)Validation allows user to access the quality of their RML rules before any knowledge graph is generated.