This is the code of paper Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction. Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang. AAAI 2020. arxiv
The results of HAKE and the baseline model ModE on WN18RR, FB15k-237 and YAGO3-10 are as follows.
MRR | HITS@1 | HITS@3 | HITS@10 | |
---|---|---|---|---|
ModE | 0.472 | 0.427 | 0.486 | 0.564 |
HAKE | 0.496 ± 0.001 | 0.452 | 0.516 | 0.582 |
MRR | HITS@1 | HITS@3 | HITS@10 | |
---|---|---|---|---|
ModE | 0.341 | 0.244 | 0.380 | 0.534 |
HAKE | 0.346 ± 0.001 | 0.250 | 0.381 | 0.542 |
MRR | HITS@1 | HITS@3 | HITS@10 | |
---|---|---|---|---|
ModE | 0.510 | 0.421 | 0.562 | 0.660 |
HAKE | 0.546 ± 0.001 | 0.462 | 0.596 | 0.694 |
bash runs.sh {train | valid | test} {ModE | HAKE} {wn18rr | FB15k-237 | YAGO3-10} <gpu_id> \
<save_id> <train_batch_size> <negative_sample_size> <hidden_dim> <gamma> <alpha> \
<learning_rate> <num_train_steps> <test_batch_size> [modulus_weight] [phase_weight]
{ | }
: Mutually exclusive items. Choose one from them.< >
: Placeholder for which you must supply a value.[ ]
: Optional items.Remark: [modulus_weight]
and [phase_weight]
are available only for the HAKE
model.
To reproduce the results of HAKE and ModE, run the following commands.
# WN18RR
bash runs.sh train HAKE wn18rr 0 0 512 1024 500 6.0 0.5 0.00005 80000 8 0.5 0.5
# FB15k-237
bash runs.sh train HAKE FB15k-237 0 0 1024 256 1000 9.0 1.0 0.00005 100000 16 3.5 1.0
# YAGO3-10
bash runs.sh train HAKE YAGO3-10 0 0 1024 256 500 24.0 1.0 0.0002 180000 4 1.0 0.5
# WN18RR
bash runs.sh train ModE wn18rr 0 0 512 1024 500 6.0 0.5 0.0001 80000 8 --no_decay
# FB15k-237
bash runs.sh train ModE FB15k-237 0 0 1024 256 1000 9.0 1.0 0.0001 100000 16
# YAGO3-10
bash runs.sh train ModE YAGO3-10 0 0 1024 256 500 24.0 1.0 0.0002 80000 4
To plot entity embeddings on a 2D plane (Figure 4 in our paper), please refer to this issue.
If you find this code useful, please consider citing the following paper.
@inproceedings{zhang2020learning,
title={Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction},
author={Zhang, Zhanqiu and Cai, Jianyu and Zhang, Yongdong and Wang, Jie},
booktitle={Thirty-Fourth {AAAI} Conference on Artificial Intelligence},
pages={3065--3072},
publisher={{AAAI} Press},
year={2020}
}
We refer to the code of RotatE. Thanks for their contributions.
If you are interested in our work, you may find the following paper useful.
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion. Zhanqiu Zhang, Jianyu Cai, Jie Wang. NeurIPS 2020. [paper] [code]