  Multilingual knowledge graph (KG) embeddings provide latent semantic
representations of entities and structured knowledge with cross-lingual
inferences, which benefit various knowledge-driven cross-lingual NLP tasks.
However, precisely learning such cross-lingual inferences is usually hindered
by the low coverage of entity alignment in many KGs. Since many multilingual
KGs also provide literal descriptions of entities, in this paper, we introduce
an embedding-based approach which leverages a weakly aligned multilingual KG
for semi-supervised cross-lingual learning using entity descriptions. Our
approach performs co-training of two embedding models, i.e. a multilingual KG
embedding model and a multilingual literal description embedding model. The
models are trained on a large Wikipedia-based trilingual dataset where most
entity alignment is unknown to training. Experimental results show that the
performance of the proposed approach on the entity alignment task improves at
each iteration of co-training, and eventually reaches a stage at which it
significantly surpasses previous approaches. We also show that our approach has
promising abilities for zero-shot entity alignment, and cross-lingual KG
completion.
