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
59 Nov 24, 2022
156 Nov 28, 2022
76 Nov 23, 2022
210 Dec 30, 2022
104 Oct 26, 2022
795 Jan 3, 2023
199 Jan 8, 2023
272 Dec 28, 2022
366 Dec 28, 2022
54 Dec 17, 2022
144 Dec 24, 2022
585 Jan 4, 2023
491 Jan 3, 2023
232 Dec 25, 2022
48 Nov 21, 2022
148 Dec 28, 2022
269 Dec 21, 2022
293 Dec 20, 2022
111 Dec 31, 2022