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GIE

This is the code of paper “Geometry Interaction Knowledge Graph Embeddings”

To preprocess the datasets, run the following commands.

cd code
python process_datasets.py
WN18RR
CUDA_VISIBLE_DEVICES=1 python3 ../run.py --dataset WN18RR --model GIE --rank 300 --regularizer N3 --reg 0.0 --optimizer Adam --max_epochs 300 --patience 15 --valid 5 --batch_size 1000 --neg_sample_size 50 --init_size 0.001 --learning_rate 0.001 --gamma 0.0 --bias learn --dtype double --double_neg --multi_c

FB237
CUDA_VISIBLE_DEVICES=7 python3 learn.py --dataset FB237 --model GIE --rank 800 --optimizer Adagrad --learning_rate 1e-1 --batch_size 2000 --regularizer N3 --reg 5e-2 --max_epochs 100 --valid 5 -train -id 0 -save

YAGO3-10
CUDA_VISIBLE_DEVICES=6 python3 learn.py --dataset YAGO3-10 --model GIE --rank 1000 --optimizer Adagrad --learning_rate 1e-1 --batch_size 1000 --regularizer N3 --reg 5e-3 --max_epochs 200 --valid 5 -train -id 0 -save