KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation

Overview

This repository contains the implementation of the paper "KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation". In this paper, we introduce enhanced KGC using LLM-generated knowledge (predictive descriptions and inverse relations) and supervised contrastive learning, achieving significant performance boosts.

Requirements

• Python 3.7 or above
• Additional dependencies are listed in requirements.txt

All experiments are conducted on a machine with 4 Quadro RTX 8000 GPUs.

Installation

1. Clone this repository

2. Install the required dependencies:

pip install -r requirements.txt

Data preparation

The link to the datasets can be found in the Google Drive folder.

Download the datasets and extract them to the data folder to get the following directory structure:

data
├── FB15k237
│   ├── entities.json
│   ├── inverse_relations.json
│   ├── test.json
│   ├── train.json
│   └── valid.json
├── WN18RR
│   ├── entities.json
│   ├── inverse_relations.json
│   ├── test.json
│   ├── train.json
│   └── valid.json
├── umls
│   ├── entities.json
│   ├── inverse_relations.json
│   ├── test.json
│   ├── train.json
│   └── valid.json

Training and evaluation

The scripts to train and evaluate a model on the WN18RR and FB15k-237 datasets are available in the scripts folder.

Acknowledgements

The code is partially borrowed from SimKGC.

Citation

If you find this work useful, please consider citing:

@misc{li2024kermitknowledgegraphcompletion,
      title={KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation}, 
      author={Haotian Li and Bin Yu and Yuliang Wei and Kai Wang and Richard Yi Da Xu and Bailing Wang},
      year={2024},
      eprint={2309.14770},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2309.14770}, 
}