Introduction

This is the PyTorch implementation of the RotatE model for knowledge graph embedding (KGE). We provide a toolkit that gives state-of-the-art performance of several popular KGE models. The toolkit is quite efficient, which is able to train a large KGE model within a few hours on a single GPU.

A faster multi-GPU implementation of RotatE and other KGE models is available in GraphVite.

Implemented features

Models:

• RotatE
• pRotatE
• TransE
• ComplEx
• DistMult

Evaluation Metrics:

• MRR, MR, HITS@1, HITS@3, HITS@10 (filtered)
• AUC-PR (for Countries data sets)

Loss Function:

• Uniform Negative Sampling
• Self-Adversarial Negative Sampling

Usage

Knowledge Graph Data:

• entities.dict: a dictionary map entities to unique ids
• relations.dict: a dictionary map relations to unique ids
• train.txt: the KGE model is trained to fit this data set
• valid.txt: create a blank file if no validation data is available
• test.txt: the KGE model is evaluated on this data set

Train

For example, this command train a RotatE model on FB15k dataset with GPU 0.

CUDA_VISIBLE_DEVICES=0 python -u codes/run.py --do_train \ --cuda \ --do_valid \ --do_test \ --data_path data/FB15k \ --model RotatE \ -n 256 -b 1024 -d 1000 \ -g 24.0 -a 1.0 -adv \ -lr 0.0001 --max_steps 150000 \ -save models/RotatE_FB15k_0 --test_batch_size 16 -de 

Check argparse configuration at codes/run.py for more arguments and more details.

Test

CUDA_VISIBLE_DEVICES=$GPU_DEVICE python -u $CODE_PATH/run.py --do_test --cuda -init $SAVE 

Reproducing the best results

To reprocude the results in the ICLR 2019 paper RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space, you can run the bash commands in best_config.sh to get the best performance of RotatE, TransE, and ComplEx on five widely used datasets (FB15k, FB15k-237, wn18, wn18rr, Countries).

The run.sh script provides an easy way to search hyper-parameters:

bash run.sh train RotatE FB15k 0 0 1024 256 1000 24.0 1.0 0.0001 200000 16 -de 

Speed

The KGE models usually take about half an hour to run 10000 steps on a single GeForce GTX 1080 Ti GPU with default configuration. And these models need different max_steps to converge on different data sets:

Results of the RotatE model

Using the library

The python libarary is organized around 3 objects:

• TrainDataset (dataloader.py): prepare data stream for training
• TestDataSet (dataloader.py): prepare data stream for evluation
• KGEModel (model.py): calculate triple score and provide train/test API

The run.py file contains the main function, which parses arguments, reads data, initilize the model and provides the training loop.

Add your own model to model.py like:

def TransE(self, head, relation, tail, mode): if mode == 'head-batch': score = head + (relation - tail) else: score = (head + relation) - tail score = self.gamma.item() - torch.norm(score, p=1, dim=2) return score 

Citation

If you use the codes, please cite the following paper:

@inproceedings{sun2018rotate, title={RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space}, author={Zhiqing Sun and Zhi-Hong Deng and Jian-Yun Nie and Jian Tang}, booktitle={International Conference on Learning Representations}, year={2019}, url={https://openreview.net/forum?id=HkgEQnRqYQ}, }