MAKAAO knowledge graph

Installation

1. Download the repository and enter it:

git clone https://github.com/f-maury/MAKAAO_core
cd MAKAAO_core

2. Create a Conda environment with the needed packages:

conda create -n makaao python=3.11
conda activate makaao
python -m pip install -r requirements.txt

3. Browse to scripts/ folder, and run the MAKAAO scripts in correct order:

cd scripts
python 00_xlsx_to_csv.py
python 01_process_makaao_core_to_tables.py
python 02_create_enrichment_tables.py
python 03_build_kg_from_tables.py
python 04_make_lite_graph_from_makaao-kg.py

4. If everything runs without issue:
   • MAKAAO KG and Lite KG will be located in kg/ folder
   • Normalized tables will be in data/processed_tables/.
   • Enrichment tables will be in data/enrichment_tables/.

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Annex

• tests/ contains the shacl_shapes.ttl file used by the GitHub CI workflow to validate some constraints on the knowledge graph.
• tests/ also contains the other unit tests used to check each script.
• Sample data are located in data/makaao_sample.csv.

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WARNING

For licensing reasons, some files that you would need to correctly run the scripts are not included in this repository. These are:

• data/en_product4.xml, obtained from Orphanet (https://www.orphadata.com/data/xml/en_product4.xml)
• data/MRCONSO.RRF, obtained from the UMLS (https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html)
• data/makaao_core.xlsx