KG-BERT: BERT for Knowledge Graph Completion

The repository is modified from pytorch-pretrained-BERT and tested on Python 3.5+.

Installing requirement packages

pip install -r requirements.txt

Data

(1) The benchmark knowledge graph datasets are in ./data.

(2) entity2text.txt or entity2textlong.txt in each dataset contains entity textual sequences.

(3) relation2text.txt in each dataset contains relation textual sequences.

Reproducing results

1. Triple Classification

WN11

python run_bert_triple_classifier.py 
--task_name kg
--do_train  
--do_eval 
--do_predict 
--data_dir ./data/WN11 
--bert_model bert-base-uncased 
--max_seq_length 20 
--train_batch_size 32 
--learning_rate 5e-5 
--num_train_epochs 3.0 
--output_dir ./output_WN11/  
--gradient_accumulation_steps 1 
--eval_batch_size 512

FB13

python run_bert_triple_classifier.py 
--task_name kg  
--do_train  
--do_eval 
--do_predict 
--data_dir ./data/FB13 
--bert_model bert-base-cased
--max_seq_length 200
--train_batch_size 32 
--learning_rate 5e-5 
--num_train_epochs 3.0 
--output_dir ./output_FB13/  
--gradient_accumulation_steps 1 
--eval_batch_size 512

2. Relation Prediction

FB15K

python3 run_bert_relation_prediction.py 
--task_name kg  
--do_train  
--do_eval 
--do_predict 
--data_dir ./data/FB15K 
--bert_model bert-base-cased
--max_seq_length 25
--train_batch_size 32 
--learning_rate 5e-5 
--num_train_epochs 20.0 
--output_dir ./output_FB15K/  
--gradient_accumulation_steps 1 
--eval_batch_size 512

3. Link Prediction

WN18RR

python3 run_bert_link_prediction.py
--task_name kg  
--do_train  
--do_eval 
--do_predict 
--data_dir ./data/WN18RR
--bert_model bert-base-cased
--max_seq_length 50
--train_batch_size 32 
--learning_rate 5e-5 
--num_train_epochs 5.0 
--output_dir ./output_WN18RR/  
--gradient_accumulation_steps 1 
--eval_batch_size 5000

UMLS

python3 run_bert_link_prediction.py
--task_name kg  
--do_train  
--do_eval 
--do_predict 
--data_dir ./data/umls
--bert_model bert-base-uncased
--max_seq_length 15
--train_batch_size 32 
--learning_rate 5e-5 
--num_train_epochs 5.0 
--output_dir ./output_umls/  
--gradient_accumulation_steps 1 
--eval_batch_size 135

FB15k-237

python3 run_bert_link_prediction.py
--task_name kg  
--do_train  
--do_eval 
--do_predict 
--data_dir ./data/FB15k-237
--bert_model bert-base-cased
--max_seq_length 150
--train_batch_size 32 
--learning_rate 5e-5 
--num_train_epochs 5.0 
--output_dir ./output_FB15k-237/  
--gradient_accumulation_steps 1 
--eval_batch_size 1500

rdenaux/kg-bert