Dependencies

• Python 3.6+
• PyTorch 1.0+
• NumPy 1.17.2+

Reproduce the Results

To reproduce the results of RQE and HRQE on WN18RR, FB15k237, WN18 and FB15K, please run the following commands.

#################################### WN18RR ####################################
# RQE
python train_models.py --model RQE --dataset WN18RR --train_times 40000 --nbatches 10 --alpha 0.02 --dimension 300 --lmbda 0.25 --lmbda_two 0.25 --ent_neg_rate 2 --valid_step 2000 

# HRQE
python train_models.py  --model HRQE --dataset WN18RR --train_times 50000 --nbatches 10 --alpha 0.1 --dimension 300 --lmbda 0.3 --lmbda_two 0.01 --ent_neg_rate 1 --valid_step 2000


#################################### FB15K237 ####################################
# RQE
python train_models.py --model RQE --dataset FB15K237 --train_times 8000 --nbatches 100 --alpha 0.02 --dimension 500 --lmbda 0.5 --lmbda_two 0.01 --ent_neg_rate 10 --valid_step 400

# HRQE
python train_models.py  --model HRQE --dataset FB15K237 --train_times 5000 --nbatches 100 --alpha 0.05 --dimension 500 --lmbda 0.5 --lmbda_two 0.01 --ent_neg_rate 10 --valid_step 400


#################################### WN18 ####################################
# RQE
python train_models.py --model RQE --dataset WN18 --train_times 4000 --nbatches 10 --alpha 0.04 --dimension 300 --lmbda 0.03 --lmbda_two 0.0 --ent_neg_rate 10 --valid_step 400

# HRQE
python train_models.py --model HRQE --dataset WN18 --train_times 8000 --nbatches 10 --alpha 0.05 --dimension 300 --lmbda 0.05 --lmbda_two 0.01 --ent_neg_rate 10 --valid_step 1000


#################################### FB15K ####################################
# RQE
python train_models.py --model RQE --dataset FB15K --train_times 2000 --nbatches 100 --alpha 0.02 --dimension 400 --lmbda 0.05 --lmbda_two 0.0 --ent_neg_rate 10 --valid_step 200

# HRQE
python train_models.py --model HRQE --dataset FB15K --train_times 4000 --nbatches 100 --alpha 0.02 --dimension 400 --lmbda 0.05 --lmbda_two 0.0 --ent_neg_rate 10 --valid_step 400

Acknowledgement

This code is based on the OpenKE project.

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

Citation

This is the code for paper "Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations". If it helps your work, please cite the following paper:

@inproceedings{yang2022learning,
  title={Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations},
  author={Yang, Jinfa and Ying, Xianghua and Shi, Yongjie and Tong, Xin and Wang, Ruibin and Chen, Taiyan and Xing, Bowei},
  booktitle={Proceedings of the 29th International Conference on Computational Linguistics},
  pages={2011--2023},
  year={2022}
}