Introduction

This documentation provides straightforward guidelines to integrate and access data in a RDF knowledge graph.

• Integrate any structured data using various solutions.
• Deploy various interfaces to consume the Knowledge Graph data.
• Deploy user-friendly web UI to access the integrated data.

The documented method allow to quickly publish structured data complying with the FAIR principles (Findable, Accessible, Interoperable, Re-usable).

Create a repository

First step is to create a repository for your project on GitHub. This will allow you to keep track of the changes to your scripts and mappings using git.

• Add a README.md file with basic informations about your project: description, how to run it, etc.
• Add a LICENSE file with the license information (e.g. MIT license)
• Follow a consistent pattern to store your mapping files and scripts.

Recommended project folder structure

root-directory

├── .github/workflows

│ ├── process-dataset1.yml

│ └── process-dataset2.yml

├── .gitignore

├── .env # Environment variables for docker-compose if needed

├── LICENSE

├── README.md

├── docker-compose.yml # If needed

├── Dockerfile # If needed

└── datasets # Folders of the different datasets mappings

└── dataset1

├── README.md # Notes about how and where to run those mappings

├── mapping # Script to download input files

│ ├── mappings.yarrr.yml # YARRRML mappings

│ └── mappings.rml.ttl # RML mappings

├── scripts # Script to download input files

│ ├── download.sh # Bash script to download data

│ └── preprocessing.py # Python script for preprocessing

└── metadata # HCLS metadata about the dataset

└── metadata-dataset1.ttl