  Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent
entities in different languages. The task of cross-lingual entity alignment is
to match entities in a source language with their counterparts in target
languages. In this work, we investigate embedding-based approaches to encode
entities from multilingual KGs into the same vector space, where equivalent
entities are close to each other. Specifically, we apply graph convolutional
networks (GCNs) to combine multi-aspect information of entities, including
topological connections, relations, and attributes of entities, to learn entity
embeddings. To exploit the literal descriptions of entities expressed in
different languages, we propose two uses of a pretrained multilingual BERT
model to bridge cross-lingual gaps. We further propose two strategies to
integrate GCN-based and BERT-based modules to boost performance. Extensive
experiments on two benchmark datasets demonstrate that our method significantly
outperforms existing systems.
