Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment

Xiaofei Shi, Yanghua Xiao

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.
Anthology ID:
D19-1075
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
813–822
Language:
URL:
https://aclanthology.org/D19-1075
DOI:
10.18653/v1/D19-1075
Bibkey:
Cite (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.
Cite (Informal):
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment (Shi & Xiao, EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/nschneid-patch-5/D19-1075.pdf
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