@inproceedings{xie-etal-2020-contextual,
title = "A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment",
author = "Xie, Zhiwen and
Zhu, Runjie and
Zhao, Kunsong and
Liu, Jin and
Zhou, Guangyou and
Huang, Jimmy Xiangji",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.520",
doi = "10.18653/v1/2020.coling-main.520",
pages = "5918--5928",
abstract = "Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.",
}
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<abstract>Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment
%A Xie, Zhiwen
%A Zhu, Runjie
%A Zhao, Kunsong
%A Liu, Jin
%A Zhou, Guangyou
%A Huang, Jimmy Xiangji
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F xie-etal-2020-contextual
%X Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.
%R 10.18653/v1/2020.coling-main.520
%U https://aclanthology.org/2020.coling-main.520
%U https://doi.org/10.18653/v1/2020.coling-main.520
%P 5918-5928
Markdown (Informal)
[A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment](https://aclanthology.org/2020.coling-main.520) (Xie et al., COLING 2020)
ACL