Boosting Cross-lingual Entity Alignment with Textual Embedding

Abstract

Multilingual knowledge graph (KG) embeddings have attracted many researchers, and benefit lots of cross-lingual tasks. The cross-lingual entity alignment task is to match equivalent entities in different languages, which can largely enrich the multilingual KGs. Many previous methods consider solely the use of structures to encode entities. However, lots of multilingual KGs provide rich entity descriptions. In this paper, we mainly focus on how to utilize these descriptions to boost the cross-lingual entity alignment. Specifically, we propose two textual embedding models called Cross-TextGCN and Cross-TextMatch to embed description for each entity. Our experiments on DBP15K show that these two textual embedding model can indeed boost the structure based cross-lingual entity alignment model.