  Abstract. Cross-lingual knowledge alignment is the cornerstone in building a
comprehensive knowledge graph (KG), which can benefit various knowledge-driven
applications. As the structures of KGs are usually sparse, attributes of
entities may play an important role in aligning the entities. However, the
heterogeneity of the attributes across KGs prevents from accurately embedding
and comparing entities. To deal with the issue, we propose to model the
interactions between attributes, instead of globally embedding an entity with
all the attributes. We further propose a joint framework to merge the
alignments inferred from the attributes and the structures. Experimental
results show that the proposed model outperforms the state-of-art baselines by
up to 38.48% HitRatio@1. The results also demonstrate that our model can infer
the alignments between attributes, relationships and values, in addition to
entities.
