Many recent works have demonstrated the benefits of knowledge graph
embeddings in completing monolingual knowledge graphs. Inasmuch as related
knowledge bases are built in several different languages, achieving
cross-lingual knowledge alignment will help people in constructing a coherent
knowledge base, and assist machines in dealing with different expressions of
entity relationships across diverse human languages. Unfortunately, achieving
this highly desirable crosslingual alignment by human labor is very costly and
errorprone. Thus, we propose MTransE, a translation-based model for
multilingual knowledge graph embeddings, to provide a simple and automated
solution. By encoding entities and relations of each language in a separated
embedding space, MTransE provides transitions for each embedding vector to its
cross-lingual counterparts in other spaces, while preserving the
functionalities of monolingual embeddings. We deploy three different techniques
to represent cross-lingual transitions, namely axis calibration, translation
vectors, and linear transformations, and derive five variants for MTransE using
different loss functions. Our models can be trained on partially aligned
graphs, where just a small portion of triples are aligned with their
cross-lingual counterparts. The experiments on cross-lingual entity matching
and triple-wise alignment verification show promising results, with some
variants consistently outperforming others on different tasks. We also explore
how MTransE preserves the key properties of its monolingual counterpart TransE.

本文提出了一种基于翻译模型的多语言知识图谱嵌入方法 MTransE 来实现跨语言的知识对齐，并采用不同的转化技术和损失函数来生成五种模型，实验证明该方法在跨语言实体匹配和三元组对齐方面取得了很好的效果。