DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning

This is the PyTorch implementation of the model DegreEmbed, proposed in the paper DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning.

Yuliang Wei, Haotian Li, Yao Wang, Guodong Xin, Hongri Liu

Environment setup

• Python >= 3.7
• PyTorch >= 1.8.0
• NumPy
• tqdm

It is recommended to create a conda virtual environment using the requirements.yaml by the following command:

conda create --name DegreEmbed --file requirements.yaml

Quick start

We provide a demo for training and evaluating our model on the Family dataset. All datasets can be found in the datasets folder.

Training

Run the following command in you shell under the root directory of this repository to train a model.

python model/main.py --dataset=family

You may check the configuration file model/configure.py for more possible hyperparameter combinations. There is also a jupyter notebook for training this model in an interactive way locating at model/train.ipynb.

When the training process finishes, there are extra files created by the script that are stored under the directory saved/family, e.g., option.txt contains hyperparameters in this experiment and prediction.txt is the prediction results on test data for computing the metrics MRR (Mean Reciprocal Rank) and Hit@k.

Evaluation

1. MRR & Hit@k

There is a separate script eval/evaluate.py to compute the MRR and Hit@k under the filtered protocol proposed in TransE, and you will see the evaluation result in your CLI.

python eval/evaluate.py --dataset=family --include_reverse --top_k=10 --filter

You may also find another shell script eval/evaluate.sh helpful to evaluate the model using different configurations in eval/evaluate.py.

2. Mined rules and saturations

You can run the notebook model/train.ipynb to train a DegreEmbed model as well as generate logic rules on the certain dataset.

The computation of saturations can be accessed in two notebooks, datasets/graph_assessment.ipynb and datasets/graph_assessment-multihop.ipynb, the former for saturations of rules with fixed length of two while the latter one allows varied lengths no longer than $L$.

For more details, please check the jupyter notebooks mentioned above.

Citation

If you find this repository useful, please cite our paper:

@misc{wei2021degreembed,
      title={DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning}, 
      author={Yuliang Wei and Haotian Li and Yao Wang and Guodong Xin and Hongri Liu},
      year={2021},
      eprint={2112.09933},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}