This repository contains the experimental code for our AAAI 2023 paper: Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding. In this paper, we propose an entity-agnostic representation learning (EARL) method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs.
We run our code mainly based on PyTorch 1.11.0 and DGL 0.8.1 with CUDA. You can install cooresponding version based on your GPU resources. Furthermore, we useargparse to parse command lines.
We put experimental datasets in ./data. Each dataset includes a train.txt, a valid.txt and a test.txt. entities.dict and relations.dict are used to map entities and relations into indices.
We use pre_process.ipynb to generate relaional features for entities (i.e., ent_rel_feat.pkl) and sample reserved entities (i.e., res_ent_0p1.pkl) for each dataset. We have generated these two files for each dataset and you can also run the process procedure yourself by using pre_process.ipynb.
Furthermore, unzip datasets before using them.
We give an example script for training and you can try the following command line:
bash script/train.shThe training process, validation results, and final test results will be printed and saved in the corresponding log file. After training, you can find training logs in the log folder and the tensorboad logs are saved in the tb_log folder.
Moreover, you can try different datasets and settings by changing arguments in the main.py.
If you use or extend our work, please cite the following paper:
@inproceedings{EARL,
author = {Mingyang Chen and
Wen Zhang and
Zhen Yao and
Yushan Zhu and
Yang Gao and
Jeff Z. Pan and
Huajun Chen},
title = {Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding},
booktitle = {{AAAI}},
year = {2023}
}