Easily generate high-quality knowledge graphs with RML.io

Generate knowledge graphs

The RMLMapper and the RMLStreamer are applications for Linux, Windows, and macOS machines for generating knowledge graphs. They both rely on declarative rules that define how the knowledge graphs are generated. Get started immediately by following the instructions on their Github repositories.

Create declarative rules

The RMLEditor and Matey are Web applications that allow users to define how knowledge graphs are generated via declarative rules. The RMLEditor offers a graph-based graphical user interface where users can create these rules by dragging and dropping different data and linking them together. Matey uses YARRRML to create rules. YARRRML is a human readable text-based representation based on YAML.

Use any type of semi-structured data

Knowledge graphs can be generated using any type of semi-structured data. There is support for different formats, such as CSV, JSON, and XML, together with support for different data sources, such as files, databases, Web APIs, and streams.

Guarantee high-quality knowledge graphs

The quality of knowledge graphs is important to ensure their usability. Validatrr is an application for Linux, Windows, and macOS machines that allows validating knowledge graphs before they are generated, by relying on declarative rules.

Where the RML.io technologies are used



The ESSENCE consortium aims to bring human perspectives into a smart city platform that collects many media types, from personal devices and public displays to augmented reality. The ultimate goal is to create interactive stories embedded in the city environment about civic actions that boost engagement in these projects.

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The DAIQUIRI project will develop AI algorithms that address current challenges associated with data overload, sensor-video matching, dynamic captioning and multi-modal stories. The outcome will be a sensor data platform and dashboard that supports media professionals in their live sports coverage and the audiences' viewing experiences.

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The DyVerSIFy project aims to develop software components and methodologies in the domains of dynamic visualization, adaptive anomaly detection and scalability to drive dynamic, adaptive and scalable sensor analytics.

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DiSSeCt aims to design distributed semantic software solutions and algorithms for continuous exchange of huge streams of data between different partners in specific ecosystems. By converting data to knowledge, and exchanging this knowledge in an intelligent, secure and dynamic manner, personalised and context-aware services can be offered to end-users.

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The MOS2S project focuses on media orchestration platforms and technologies that allow devices, data and media streams to be orchestrated into a rich and coherent media experience on various end-user devices. Applications include crowd journalism and live events (experience and entertainment).

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