Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we introduce the topic entity graph, a local sub-graph of an entity, to represent entities with their contextual information in KG. From this view, the KB-alignment task can be formulated as a graph matching problem; and we further propose a graph-attention based solution, which first matches all entities in two topic entity graphs, and then jointly model the local matching information to derive a graph-level matching vector. Experiments show that our model outperforms previous state-of-the-art methods by a large margin.

翻译：先前的跨语言知识图(KG)校准研究依赖于仅仅从单语KG结构信息中得出的实体嵌入,这种嵌入在两个KG中具有不同事实的匹配实体中可能失败。 在本文中,我们介绍了一个实体的本地子集,即主题实体图,以在KG中代表实体。从这个观点看,KB对齐任务可以作为一个图表匹配问题来拟订;我们进一步提出一个基于图形的关注解决方案,首先在两个主题实体图中匹配所有实体,然后共同模拟本地匹配信息,以得出一个图形水平的匹配矢量。实验表明,我们的模型以大幅度比以往最先进的方法要好。