Cross-lingual entity alignment aims to find entities in knowledge graphs of different languages that point to the same objects in the real world. Traditional cross-lingual entity alignment methods usually rely solely on the internal structure information of the knowledge graph

but in fact entity description information provided by some knowledge graphs can also be utilized. This paper proposes an entity alignment model that combines the internal structure information of the knowledge graph with the entity description information for cross-lingual entity alignment. The model first finds the entity pairs that may be aligned by training the knowledge embeddings based on the structure information of the knowledge graph

and then uses entity descriptions to select the final aligned entity pairs based on the improved optimal alignment similarity model. Finally

the model iteratively align the first two steps to find more aligned entity pairs until the end of the training. The experimental results show that compared with the benchmark algorithms

the proposed model can achieve relatively good results in cross-lingual entity alignment task.