  Entity alignment is the task of finding entities representing the same
real-world object in two knowledge graphs(KGs). Cross-lingual knowledge graph
entity alignment aims to discover the cross-lingual links in the multi-language
KGs, which is of great significance to the NLP applications and multi-language
KGs fusion. In the task of aligning cross-language knowledge graphs, the
structures of the two graphs are very similar, and the equivalent entities
often have the same subgraph structure characteristics. The traditional GCN
method neglects to obtain structural features through representative parts of
the original graph and the use of adjacency matrix is not enough to effectively
represent the structural features of the graph. In this paper, we introduce the
subgraph network (SGN) method into the GCN-based cross-lingual KG entity
alignment method. In the method, we extracted the first-order subgraphs of the
KGs to expand the structural features of the original graph to enhance the
representation ability of the entity embedding and improve the alignment
accuracy. Experiments show that the proposed method outperforms the
state-of-the-art GCN-based method.
