Source code for "Unsupervised Entity Alignment for Temporal Knowledge Graphs". The ACM Web Conference 2023.
A 3-minute short video briefly describing our method can be found Here.
We thank TEA-GNN for providing the datasets.
-ICEWS05-15
-YAGO-WIKI50K
-YAGO-WIKI20K
ent_ids_1: ids for entities in source KG;
ent_ids_2: ids for entities in target KG;
ref_ent_ids: entity links encoded by ids;
triples_1: relation triples encoded by ids in source KG;
triples_2: relation triples encoded by ids in target KG;
rel_ids_1: ids for relations in source KG;
rel_ids_2: ids for relations in target KG;
sup_pairs + ref_pairs: entity alignments
To perform EA on ICEWS05-15 in unsupervised manner:
python main.py --ds 0 --unsup
To perform EA on YAGO-WIKI50K in Less seed setting:
python main.py --ds 1 --train_ratio 20
To perform EA on YAGO-WIKI20K in Normal setting and evaluate separately:
python main.py --ds 2 --sep_eval
Note: Part of the code needs to be run on CPU. We plan to fix this issue in the future.
We refer to the code of Dual-AMN, TEA-GNN, and SEU. Thanks for their great contributions!
Code credit to Xiaoze Liu and Junyang Wu.
If you find this work useful, please cite
@inproceedings{DBLP:conf/www/LiuW00G23,
author = {Xiaoze Liu and
Junyang Wu and
Tianyi Li and
Lu Chen and
Yunjun Gao},
title = {Unsupervised Entity Alignment for Temporal Knowledge Graphs},
booktitle = {Proceedings of the {ACM} Web Conference 2023, {WWW} 2023, Austin,
TX, USA, 30 April 2023 - 4 May 2023},
pages = {2528--2538},
publisher = {{ACM}},
year = {2023},
url = {https://doi.org/10.1145/3543507.3583381},
doi = {10.1145/3543507.3583381},
}