This is a Python implementation of
A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
Jianheng Tang, Kangfei Zhao, Jia Li
ACL 2023 (Findings)
- python 3.10.6
- pytorch 1.13.0
- SentenceTransformer 2.2.2
- argparse 1.1
- dgl 0.9.1
All datasets and pretrained embeddings used in the paper are on google drive. Download and unzip all files in the data
folder.
Run bash run.sh
to reproduce all the experimental results in our paper.
@inproceedings{FGWEA,
title = "A Fused {G}romov-{W}asserstein Framework for Unsupervised Knowledge Graph Entity Alignment",
author = "Tang, Jianheng and
Zhao, Kangfei and
Li, Jia",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
url = "https://aclanthology.org/2023.findings-acl.205",
doi = "10.18653/v1/2023.findings-acl.205",
pages = "3320--3334",
}