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        <title>Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks</title>
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    <abstract>Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the cross-lingual links in multilingual KGs. In this paper, we propose a novel approach for cross-lingual KG alignment via graph convolutional networks (GCNs). Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space. Entity alignments are discovered based on the distances between entities in the embedding space. Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments. In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1032</identifier>
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%0 Conference Proceedings
%T Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
%A Wang, Zhichun
%A Lv, Qingsong
%A Lan, Xiaohan
%A Zhang, Yu
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wang-etal-2018-cross-lingual
%X Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the cross-lingual links in multilingual KGs. In this paper, we propose a novel approach for cross-lingual KG alignment via graph convolutional networks (GCNs). Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space. Entity alignments are discovered based on the distances between entities in the embedding space. Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments. In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.
%R 10.18653/v1/D18-1032
%U https://aclanthology.org/D18-1032
%U https://doi.org/10.18653/v1/D18-1032
%P 349-357

Markdown (Informal)

[Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks](https://aclanthology.org/D18-1032) (Wang et al., EMNLP 2018)

• Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (Wang et al., EMNLP 2018)

ACL

• Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang. 2018. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 349–357, Brussels, Belgium. Association for Computational Linguistics.