Our proposed method REA (Robust Entity Alignment) consists of two components: noise detection and noise-aware entity alignment.
The noise detection is designed by following the adversarial training principle. The noise-aware entity alignment is devised by leveraging graph neural network based knowledge graph encoder as the core. In order to mutually boost the performance of the two components, we propose a unified reinforced training strategy to combine them.
REA is a plug-and-play strategy to mitigate the effect of noise in the given labeled entity pairs for entity alignment problem. The idea also can be easily developed for other alignment algorithms.
Contact: Shichao Pei (shichao.pei@kaust.edu.sa)
- python>=3.5
- tensorflow>=1.10.1
- scipy>=1.1.0
- networkx>=2.2
python3 train.py --lang zh_en
Datasets are from JAPE.
Please refer to our paper.
@inproceedings{pei2020rea,
title={Rea: Robust cross-lingual entity alignment between knowledge graphs},
author={Pei, Shichao and Yu, Lu and Yu, Guoxian and Zhang, Xiangliang},
booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={2175--2184},
year={2020}
}