Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding

  • Zequn Sun
  • Wei HuEmail author
  • Chengkai Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10587)


Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.


Cross-lingual entity alignment Knowledge base embedding Joint attribute-preserving embedding 



This work is supported by the National Natural Science Foundation of China (Nos. 61370019, 61572247 and 61321491).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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