Abstract: When distinguishing the different representation effects of different neighbors on the central entity,existing entity alignment methods mostly use feature similarity or local feature information of the relationships between entities to calculate attention coefficients.Those methods ignore the global information of relationships,and cannot effectively distinguish the effect of different information on entity alignment.To address this problem,this paper proposes an entity alignment model,which assigns different weights to different types of relationships based on the frequency of(relationship,neighbor) appearing in the entire graph,then integrates the weights into GAT to obtain a variant attention mechanism for aggregating different neighbors.Meanwhile,different attribute values are aggregated in a similar way by using the frequency information of(attributes,attribute values) in the entire graph.After that,combining the structure and entity name embedding with two types of information respectively to obtain two embedding representations of the central entity.Finally,entity alignment is performed based on the weighted distance between the two embedding representations,aims to distinguish the different effects of relationship and attribute information on the entity alignment.Experimental results show that this model is superior to other methods in the three cross lingual datasets of DBP15K,Hit@1 increases by a maximum of 2.15% compare to the optimal method,besides,results also show significant improvement in both Hit@10 and MRR,which indicate that the proposed model can effectively enhance the accuracy of entity alignment.

Key words: Entity alignment, Knowledge graph, Variant attention mechanism, Attribute-aware