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.

本文研究利用基于嵌入的方法，利用多语言知识图谱上实体的文字描述，通过共训两种嵌入模型，即一个多语言知识图谱嵌入模型和一个多语言文字描述嵌入模型，进行半监督学习。实验结果表明，该方法在实体对齐任务中的性能随共训迭代而提高，最终明显优于之前的方法。此外，该方法具有零样本实体对齐和跨语言知识图谱完成的良好能力。