Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

TL;DR

This paper introduces a joint attribute-preserving embedding model for cross-lingual entity alignment that effectively encodes knowledge bases in a shared space, outperforming existing methods without relying on machine translation.

Contribution

The proposed model uniquely combines structure and attribute correlations in a unified embedding space for improved cross-lingual entity alignment.

Findings

Significantly outperforms state-of-the-art embedding methods

Effectively leverages attribute correlations in knowledge bases

Does not depend on machine translation quality

Abstract

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.