RML.io Dashboard

Web application which use RML.io to generate high-quality Linked Data, in order to create knowledge graphs.

---

Generation of the RML file

First, you need to create or load a project:

Once you get into your project, you have three points to respect, in an arbitrary order:

1. add your source file(s): input file(s) and/or RML file(s);
2. add your processor(s) by specifying:
   • its type of the RML file (e.g. N-Quads, TriG, Turtle);
   • its mapping configuration (generated from Matey).
3. click on deploy and pick your deployment preferences (run your workspace and/or download it).

NOTE: a source file can be linked to 1 or n processors.

Getting Started

Start by cloning the repository:

git clone https://github.com/oSoc20/rml-workbench-front-end

Next, let's install the dependencies:

yarn install

Finally, launch the web server:

yarn start

The website is now available locally on http://localhost:3000!

NOTE: for ease of development, the backend is available on another repository.

Use-cases

A set of use cases of knowledge graphs is available so that you can have a better global vision of the panel of possibilities that is provided to you.

Vocabulary

The following is a non-exhaustive list of commonly used vocabulary:

Processor

Generates a target according to a mapping config.

Source

An input file (e.g. CSV, JSON, XML) or an RML file (e.g. N-Quads, TriG, Turtle).

Target

A RML file (e.g. N-Quads, TriG, Turtle).

FAQ

What's knowledge graphs?

A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge. With a knowledge graph, a machine machine can easily understand and extract the information.

Where knowledge graphs are used?

Knowledge graphs are often used in various areas of machine learning (ML), natural language processing (NLP) and search.

License

Code is under the MIT License.