  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.
