An Adversarial Transfer Network for Knowledge Representation Learning

Given a target knowledge graph, ATransN improves knowledge representation learning by transferring knowledge from one or more teacher knowledge graphs to the target one through an aligned entity set without explicit data leakage. To handle the problem of possible distribution differences between teacher and target knowledge graphs, we introduce an adversarial adaption module. Please refer to the paper for details.

Dependencies

• Python 3.x
• PyTorch 1.7

Dataset

All datasets are included in the data directory.

• CN3l (EN-DE)
• WK3l-15k (EN-FR)
• DWY100k

Hint

• CN3l_EN merges CN3l_EN_D and CN3l_EN_F

• WK3l-15k_EN merges WK3l-15k_EN_D and WK3l-15k_EN_F

• DBP_DB merges DBP_DB_W and DBP_DB_Y

• cn3l_en_d_de_aligned_entity_id.txt corresponds to (CN3l_EN_D, CN3l_DE)

• cn3l_en_de_aligned_entity_id.txt corresponds to (CN3l_EN, CN3l_DE)

Training model

• TransE model

python run_kge.py --seed 2020 --steps 10000 --eval_steps 2000 --save_steps 2000 --data_path ../data/CN3l_DE --save_path ../dumps --num_workers 0 --margin 8.0 --kge_model TransE --emb_dim 200 --test_batch 512 --kge_batch 128 --num_neg_samples 128 --gpu_id 0;

• DistMult model

python run_kge.py --seed 2020 --steps 10000 --eval_steps 2000 --save_steps 2000 --data_path ../data/CN3l_DE --save_path ../dumps --num_workers 0 --margin 1.0 --kge_model DistMult --emb_dim 200 --test_batch 512 --kge_batch 128 --num_neg_samples 128 --gpu_id 1;

• ComplEx model

python run_kge.py --seed 2020 --steps 10000 --eval_steps 2000 --save_steps 2000 --data_path ../data/CN3l_DE --save_path ../dumps --num_workers 0 --margin 1.0 --kge_model ComplEx --emb_dim 100 --test_batch 512 --kge_batch 128 --num_neg_samples 128 --gpu_id 2;

• RotatE model

python run_kge.py --seed 2020 --steps 10000 --eval_steps 2000 --save_steps 2000 --data_path ../data/CN3l_DE --save_path ../dumps --num_workers 0 --margin 8.0 --kge_model RotatE --emb_dim 100 --test_batch 512 --kge_batch 128 --num_neg_samples 128 --gpu_id 3;

• ATransN model

python run_wgan_kge.py --seed 2020 --steps 10000 --eval_steps 2000 --save_steps 100000 --save_path ../dumps --teacher_data_path ../data/CN3l_EN_D --student_data_path ../data/CN3l_DE --shared_entity_path ../data/SHARED/cn3l_en_d_de_aligned_entity_id.txt --teacher_model_path ../dumps/TransE_CN3l_EN_D_KLR0.0010_ED200_MG8_NS128_SD2020_2020_09_21_15_47_52/ --num_workers 0 --margin 8.0 --kge_model TransE --emb_dim 200 --test_batch 512 --kge_batch 128 --num_neg_samples 128 --kge_alpha 0.0 --kge_beta 0.8 --gpu_id 0;

• CTransN model

Uncomment line 362, 398 in run_wgan_kge.py and comment line 363, 399 in run_wgan_kge.py

python run_wgan_kge.py --seed 2020 --steps 10000 --eval_steps 2000 --save_steps 100000 --save_path ../dumps --teacher_data_path ../data/CN3l_EN_D --student_data_path ../data/CN3l_DE --shared_entity_path ../data/SHARED/cn3l_en_d_de_aligned_entity_id.txt --teacher_model_path ../dumps/TransE_CN3l_EN_D_KLR0.0010_ED200_MG8_NS128_SD2020_2020_09_21_15_47_52/ --num_workers 0 --margin 8.0 --kge_model TransE --emb_dim 200 --test_batch 512 --kge_batch 128 --num_neg_samples 128 --kge_alpha 1.0 --kge_beta 1.0 --gpu_id 0;

Citation

Please cite the following paper if you use this code in your work.

@inproceedings{
    wang2021atransn,
    title={An Adversarial Transfer Network for Knowledge Representation Learning},
    author={Huijuan Wang and Shuangyin Li and Rong Pan},
    booktitle={The Web Conference},
    year={2021}
}