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        <title>Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment</title>
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    <abstract>Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision. To solve this issue, we propose a new embedding-based framework. Through defining dot product-based functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations. We calibrate embeddings of different KGs via a small set of pre-aligned seeds. We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end. Experimental results (especially with the metric Hits@1) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment
%A Shi, Xiaofei
%A Xiao, Yanghua
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F shi-xiao-2019-modeling
%X Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision. To solve this issue, we propose a new embedding-based framework. Through defining dot product-based functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations. We calibrate embeddings of different KGs via a small set of pre-aligned seeds. We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end. Experimental results (especially with the metric Hits@1) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance.
%R 10.18653/v1/D19-1075
%U https://aclanthology.org/D19-1075
%U https://doi.org/10.18653/v1/D19-1075
%P 813-822

Markdown (Informal)

[Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment](https://aclanthology.org/D19-1075) (Shi & Xiao, EMNLP-IJCNLP 2019)

• Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment (Shi & Xiao, EMNLP-IJCNLP 2019)

ACL

• Xiaofei Shi and Yanghua Xiao. 2019. Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 813–822, Hong Kong, China. Association for Computational Linguistics.