HAKE: Hierarchy-Aware Knowledge Graph Embedding

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

Dependencies

• Python 3.6+
• PyTorch 1.0+

Results

The results of HAKE and the baseline model ModE on WN18RR, FB15k-237 and YAGO3-10 are as follows.

WN18RR

FB15k-237

YAGO3-10

Running the code

Usage

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.

HAKE

# 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

ModE

# 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

Citation

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}
}

Acknowledgement

We refer to the code of RotatE. Thanks for their contributions.

Other Repositories

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]