SNOMED-CT Knowledge Graph Embeddings

This repository accompanies the paper Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings: https://www.aclweb.org/anthology/2020.bionlp-1.18.pdf

Make sure to have graphvite and pytorch installed, and replace the application.py file in the graphvite site-packages directory in your environment with the application.py file in this repo (made some modification to include different functionalities for training, evaluating, and visualization).

Downloads

MRCONSO.RRF, MRREL.RRF, and MRSTY.RRF from the UMLS
transitive closure resources from https://www.nlm.nih.gov/healthit/snomedct/us_edition.html
semantic group files from https://metamap.nlm.nih.gov/SemanticTypesAndGroups.shtml

Preprocessing and dataset building

follow umls_preprocess.txt to get active_concepts.txt, active_relations.txt, and semantic_types.txt
follow transitive_closure.ipynb to get transitive_closure_full.txt
follow umls_utils.ipynb to further subset concepts and relations according to semantic types/groups, and create datasets for training/validation/testing.

Train, eval, and visualize

graphvite_experiments.py: runs the training and evaluation

To train all 5 KGE models:

for X in TransE DistMult SimplE ComplEx RotatE; do
  OUTPUT_DIR='output directory'
  DATA_DIR='data directory'
  GPU=0

  mkdir -p $OUTPUT_DIR

  python graphvite_experiments.py \
    --data_dir $DATA_DIR \
    --transitive_closure \
    --model_name $X \
    --num_epoch 200 \
    --dim 512 \
    --gpu $GPU \
    --num_negative 60 \
    --margin 8 \
    --learning_rate 5e-4 \
	--output_dir=$OUTPUT_DIR \
    --per_relation_eval \
    --target both \
    --save_model $X
    
done

entity_classification.py: runs entity classification (requires cui2vec and snomed2vec)
visualization.py: visualizes the trained embeddings

Pretrained embeddings

Here's the GDrive link to download pretrained embeddings for the 5 KGE models: https://drive.google.com/drive/folders/1HN-TfZT9S3qHBIZO4_CItn4OWbuKZtll?usp=sharing

Comments

When I was working on this project almost a year ago, I chose Graphvite as the package for kge training due to its advantages at the time. There has been a lot of progress in the field since then, and currently my personal favorite for training KGEs is dgl-ke: https://github.com/awslabs/dgl-ke

krishanudb/snomed_kge