Multi-information Optimized Entity Alignment Model Based on Graph Neural Network

Entity alignment is a key step in knowledge fusion,which aims to discover entity pairs with corresponding relations between knowledge graphs.Knowledge fusion enables a more extensive and accurate services for further knowledge graph applications.However,the entity names and relations are used insufficiently by most of the state-of-the-art models of entity alignment.After obtaining the vector representation of the entity,generally the alignment relations among the entities are obtained through single iterative strategy or direct calculation,while ignoring some valuable information,so that the result of entity alignment is not ideal.In view of the above problems,a multi-information optimized entity alignment model based on graph neural network(MOGNN) is proposed.Firstly,the input of the model fuses word information and character information in the entity name,and the vector representation of relations is learnt through attention mechanism.After transmitting the information by utilizing relations,MOGNN corrects the initial entity alignment matrix based on the pre-alignment results of entities and relations,and finally employs the deferred acceptance algorithm to further correct the misaligned results.The proposed model is validated on three subsets of DBP15K,and compared with the baseline models.Compared with the baseline models,Hits@1 increases by 4.47%,0.82% and 0.46%,Hits@10 and MRR have also achieved impressive results,and the effectiveness of the model is further verifies by ablation experiments.Therefore,more accurate entity alignment results can be obtained with the proposed model.