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.
we apply graph convolutional networks (GCNs) to combine multiaspect information of entities, including topological connections, relations, and attributes of entities, to learn entity embeddings.