STAR_KGC
This repo contains the source code of the paper accepted by WWW'2021. "Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion"(WWW 2021).
1. Thanks
The repository is partially based on huggingface transformers, KG-BERT and RotatE.
2. Installing requirement packages
- conda create -n StAR python=3.6
- source activate StAR
- pip install numpy torch tensorboardX tqdm boto3 requests regex sacremoses sentencepiece matplotlib
2.1 Optional package (for mixed float Computation)
- git clone https://github.com/NVIDIA/apex
- cd apex
- pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
3. Dataset
-
WN18RR, FB15k-237, UMLS
- Train and test set in ./data
- As validation on original dev set is costly, we validated the model on dev subset during training.
- The dev subset of WN18RR is provided in ./data/WN18RR called new_dev.dict. Use below commands to get the dev subset for WN18RR (FB15k-237 is similar without the --do_lower_case) used in training process.
CUDA_VISIBLE_DEVICES=0 \ python get_new_dev_dict.py \ --model_class bert \ --weight_decay 0.01 \ --learning_rate 5e-5 \ --adam_epsilon 1e-6 \ --max_grad_norm 0. \ --warmup_proportion 0.05 \ --do_train \ --num_train_epochs 7 \ --dataset WN18RR \ --max_seq_length 128 \ --gradient_accumulation_steps 4 \ --train_batch_size 16 \ --eval_batch_size 128 \ --logging_steps 100 \ --eval_steps -1 \ --save_steps 2000 \ --model_name_or_path bert-base-uncased \ --do_lower_case \ --output_dir ./result/WN18RR_get_dev \ --num_worker 12 \ --seed 42 \CUDA_VISIBLE_DEVICES=0 \ python get_new_dev_dict.py \ --model_class bert \ --weight_decay 0.01 \ --learning_rate 5e-5 \ --adam_epsilon 1e-6 \ --max_grad_norm 0. \ --warmup_proportion 0.05 \ --do_eval \ --num_train_epochs 7 \ --dataset WN18RR \ --max_seq_length 128 \ --gradient_accumulation_steps 4 \ --train_batch_size 16 \ --eval_batch_size 128 \ --logging_steps 100 \ --eval_steps 1000 \ --save_steps 2000 \ --model_name_or_path ./result/WN18RR_get_dev \ --do_lower_case \ --output_dir ./result/WN18RR_get_dev \ --num_worker 12 \ --seed 42 \ -
NELL-One
- We reformat original NELL-One as the three benchmarks above.
- Please run the below command to get the reformatted data.
python reformat_nell_one.py --data_dir path_to_downloaded --output_dir ./data/NELL_standard
4. Training and Test (StAR)
Run the below commands for reproducing results in paper. Note, all the eval_steps is set to -1 to train w/o validation and save the last checkpoint, because standard dev is very time-consuming. This can get similar results as in the paper.
4.1 WN18RR
CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
--model_class roberta \
--weight_decay 0.01 \
--learning_rate 1e-5 \
--adam_betas 0.9,0.98 \
--adam_epsilon 1e-6 \
--max_grad_norm 0. \
--warmup_proportion 0.05 \
--do_train --do_eval \
--do_prediction \
--num_train_epochs 7 \
--dataset WN18RR \
--max_seq_length 128 \
--gradient_accumulation_steps 4 \
--train_batch_size 16 \
--eval_batch_size 128 \
--logging_steps 100 \
--eval_steps 4000 \
--save_steps 2000 \
--model_name_or_path roberta-large \
--output_dir ./result/WN18RR_roberta-large \
--num_worker 12 \
--seed 42 \
--cls_method cls \
--distance_metric euclidean \
CUDA_VISIBLE_DEVICES=2 \
python run_link_prediction.py \
--model_class bert \
--weight_decay 0.01 \
--learning_rate 5e-5 \
--adam_betas 0.9,0.98 \
--adam_epsilon 1e-6 \
--max_grad_norm 0. \
--warmup_proportion 0.05 \
--do_train --do_eval \
--do_prediction \
--num_train_epochs 7 \
--dataset WN18RR \
--max_seq_length 128 \
--gradient_accumulation_steps 4 \
--train_batch_size 16 \
--eval_batch_size 128 \
--logging_steps 100 \
--eval_steps 4000 \
--save_steps 2000 \
--model_name_or_path bert-base-uncased \
--do_lower_case \
--output_dir ./result/WN18RR_bert \
--num_worker 12 \
--seed 42 \
--cls_method cls \
--distance_metric euclidean \
4.2 FB15k-237
CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
--model_class roberta \
--weight_decay 0.01 \
--learning_rate 1e-5 \
--adam_betas 0.9,0.98 \
--adam_epsilon 1e-6 \
--max_grad_norm 0. \
--warmup_proportion 0.05 \
--do_train --do_eval \
--do_prediction \
--num_train_epochs 7. \
--dataset FB15k-237 \
--max_seq_length 100 \
--gradient_accumulation_steps 4 \
--train_batch_size 16 \
--eval_batch_size 128 \
--logging_steps 100 \
--eval_steps -1 \
--save_steps 2000 \
--model_name_or_path roberta-large \
--output_dir ./result/FB15k-237_roberta-large \
--num_worker 12 \
--seed 42 \
--fp16 \
--cls_method cls \
--distance_metric euclidean \
4.3 UMLS
CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
--model_class roberta \
--weight_decay 0.01 \
--learning_rate 1e-5 \
--adam_betas 0.9,0.98 \
--adam_epsilon 1e-6 \
--max_grad_norm 0. \
--warmup_proportion 0.05 \
--do_train --do_eval \
--do_prediction \
--num_train_epochs 20 \
--dataset UMLS \
--max_seq_length 16 \
--gradient_accumulation_steps 1 \
--train_batch_size 16 \
--eval_batch_size 128 \
--logging_steps 100 \
--eval_steps -1 \
--save_steps 200 \
--model_name_or_path roberta-large \
--output_dir ./result/UMLS_model \
--num_worker 12 \
--seed 42 \
--cls_method cls \
--distance_metric euclidean
4.4 NELL-One
CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
--model_class bert \
--do_train --do_eval \usepacka--do_prediction \
--warmup_proportion 0.1 \
--learning_rate 5e-5 \
--num_train_epochs 8. \
--dataset NELL_standard \
--max_seq_length 32 \
--gradient_accumulation_steps 1 \
--train_batch_size 16 \
--eval_batch_size 128 \
--logging_steps 100 \
--eval_steps -1 \
--save_steps 2000 \
--model_name_or_path bert-base-uncased \
--do_lower_case \
--output_dir ./result/NELL_model \
--num_worker 12 \
--seed 42 \
--fp16 \
--cls_method cls \
--distance_metric euclidean
5. StAR_Self-Adp
5.1 Data preprocessing
-
Get the trained model of RotatE, more details please refer to RotatE.
-
Run the below commands sequentially to get the training dataset of StAR_Self-Adp.
- Run the run_get_ensemble_data.py in ./StAR
CUDA_VISIBLE_DEVICES=0 python run_get_ensemble_data.py \ --dataset WN18RR \ --model_class roberta \ --model_name_or_path ./result/WN18RR_roberta-large \ --output_dir ./result/WN18RR_roberta-large \ --seed 42 \ --fp16- Run the ./codes/run.py in rotate. (please replace the TRAINED_MODEL_PATH with your own trained model's path)
CUDA_VISIBLE_DEVICES=3 python ./codes/run.py \ --cuda --init ./models/RotatE_wn18rr_0 \ --test_batch_size 16 \ --star_info_path /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large \ --get_scores --get_model_dataset
5.2 Train and Test
- Run the run.py in ./StAR/ensemble. Note the
--modeshould be alternate inheadandtail, and perform a average operation to get the final results. - Note: Please replace YOUR_OUTPUT_DIR, TRAINED_MODEL_PATH and StAR_FILE_PATH in ./StAR/peach/common.py with your own paths to run the command and code.
CUDA_VISIBLE_DEVICES=2 python run.py \
--do_train --do_eval --do_prediction --seen_feature \
--mode tail \
--learning_rate 1e-3 \
--feature_method mix \
--neg_times 5 \
--num_train_epochs 3 \
--hinge_loss_margin 0.6 \
--train_batch_size 32 \
--test_batch_size 64 \
--logging_steps 100 \
--save_steps 2000 \
--eval_steps -1 \
--warmup_proportion 0 \
--output_dir /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large_ensemble \
--dataset_dir /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large \
--context_score_path /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large \
--translation_score_path /home/wangbo/workspace/StAR_KGC-master/rotate/models/RotatE_wn18rr_0 \
--seed 42
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