KEEN: Hyperbolic Knowledge graph Embedding in Extended Poincaré ball

This repository contains our implementation of paper Hyperbolic Knowledge Graph Embedding in Extended Poincaré Ball. Xingchen Zhou, Peng Wang, Zhe Pan.

1. Dependencies

• Python 3.6+
• PyTorch 1.0+
• numpy 1.14+

2. Results

The results of KEEN on WN18RR, FB15k-237 and YAGO3-10 are shown as follows.

Datasets	MRR	H@1	H@3	H@10
WN18RR	.488	.448	.502	.570
FB15k-237	.340	.243	.379	.541
YAGO3-10	.499	.411	.553	.667

3. Instructions for running

To analyze KEEN's performance on the datasets, please run our code as follows:

3.1 WN18RR

CUDA_VISIBLE_DEVICES=0 python -u runs.py --do_train --do_valid --do_test --data_path ./data/wn18rr/ --model KEEN -n 1024 -b 256 -d 512 -g 6.0 -a 0.5 -lr 0.0001 --max_steps 80000 -save models/KEEN_wn18rr --test_batch_size 8 --cuda

3.2 FB15k-237

CUDA_VISIBLE_DEVICES=1 python -u codes/runs.py --do_train --do_valid --do_test --data_path ./data/FB15k-237/ --model KEEN -n 1024 -b 256 -d 1000 -g 9.0 -a 1.0 -lr 0.0001 --max_steps 80000 -save models/KEEN_FB15k-237 --test_batch_size 8 --cuda

3.3 YAGO3-10

CUDA_VISIBLE_DEVICES=2 python -u codes/runs.py --do_train --do_valid --do_test --data_path ./data/YAGO3-10/ --model KEEN -n 512 -b 256 -d 512 -g 24.0 -a 1.0 -lr 0.002 --max_steps 180000 -save models/KEEN_YAGO3-10 --test_batch_size 8 --cuda

4. Acknowledgement

We refer to the code of KGE-HAKE. Thanks for their contributions.

5. Licences

Every source code file written exclusively by the author of this repo is licensed under Apache License Version 2.0. For more information, please refer to the license.