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DURA: Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
This is the code of paper Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion. Zhanqiu Zhang, Jianyu Cai, Jie Wang. NeurIPS 2020. [arXiv] [NeurIPS-Official]
Dependencies
- Python 3.6+
- PyTorch 1.0~1.7
- NumPy 1.17.2+
- tqdm 4.41.1+
Results
The results of DURA on WN18RR, FB15k-237 and YAGO3-10 are as follows.
<p align="center"> <img src="./result.png"> </p>Reproduce the Results
1. Preprocess the Datasets
To preprocess the datasets, run the following commands.
cd code
python process_datasets.py
Now, the processed datasets are in the data directory.
2. Reproduce the Results
To reproduce the results of CP, ComplEx and RESCAL with the DURA regularizer on WN18RR, FB15k237 and YAGO3-10, please run the following commands.
#################################### WN18RR ####################################
# CP
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset WN18RR --model CP --rank 2000 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 100 --regularizer DURA --reg 1e-1 --max_epochs 200 \
--valid 5 -train -id 0 -save -weight
# ComplEx
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset WN18RR --model ComplEx --rank 2000 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 100 --regularizer DURA_W --reg 1e-1 --max_epochs 50 \
--valid 5 -train -id 0 -save -weight
# RESCAL
CUDA_VISIBLE_DEVICES=2 python learn.py --dataset WN18RR --model RESCAL --rank 256 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 1024 --regularizer DURA_RESCAL --reg 1e-1 --max_epochs 200 \
--valid 5 -train -id 0 -save -weight
#################################### FB237 ####################################
# CP
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset FB237 --model CP --rank 2000 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 100 --regularizer DURA_W --reg 5e-2 --max_epochs 200 \
--valid 5 -train -id 0 -save
# ComplEx
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset FB237 --model ComplEx --rank 2000 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 100 --regularizer DURA_W --reg 5e-2 --max_epochs 200 \
--valid 5 -train -id 0 -save
# RESCAL
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset FB237 --model RESCAL --rank 512 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 512 --regularizer DURA_RESCAL --reg 5e-2 --max_epochs 200 \
--valid 5 -train -id 0 -save
#################################### YAGO3-10 ####################################
# CP
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset YAGO3-10 --model CP --rank 1000 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 1000 --regularizer DURA_W --reg 5e-3 --max_epochs 200 \
--valid 5 -train -id 0 -save -weight
# ComplEx
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset YAGO3-10 --model ComplEx --rank 1000 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 1000 --regularizer DURA_W --reg 5e-3 --max_epochs 200 \
--valid 5 -train -id 0 -save
# RESCAL
CUDA_VISIBLE_DEVICES=0 python learn.py --dataset YAGO3-10 --model RESCAL --rank 512 --optimizer Adagrad \
--learning_rate 1e-1 --batch_size 1024 --regularizer DURA_RESCAL_W --reg 5e-3 --max_epochs 200 \
--valid 5 -train -id 0 -save -weight
Citation
If you find this code useful, please consider citing the following paper.
@inproceedings{NEURIPS2020_f6185f0e,
author = {Zhang, Zhanqiu and Cai, Jianyu and Wang, Jie},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {21604--21615},
publisher = {Curran Associates, Inc.},
title = {Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion},
url = {https://proceedings.neurips.cc/paper/2020/file/f6185f0ef02dcaec414a3171cd01c697-Paper.pdf},
volume = {33},
year = {2020}
}
Acknowledgement
We refer to the code of kbc. Thanks for their contributions.
Other Repositories
If you are interested in our work, you may find the following paper useful.
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction. Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang, Jie Wang. AAAI 2020. [paper] [code]