自适应属性选择的实体对齐方法

现有实体对齐方法普遍存在传统方法依赖外部信息和人工构建特征，而基于表示学习的方法忽略了知识图谱中的结构信息的问题。针对上述问题，提出自适应属性选择的实体对齐方法，融合实体的语义和结构信息训练基于两个图谱联合表示学习的实体对齐模型。提出使用基于自适应属性选择的属性强约束模型，根据数据集特征自动生成最优属性类型和权重约束，提升实体对齐效果。两个实际数据集上的试验表明,该方法与传统表示学习方法相比准确率最高提升了约11%。

Entity alignment method based on adaptive attribute selection

Most existing entity alignment methods typically relied on external information and required expensive manual feature construction to complete alignment. Knowledge graph-based methods used only semantic information and failed to use structural information. Therefore, this paper proposed a new entity alignment method based on adaptive attribute selection, training an entity alignment model based on the joint embedding of the two knowledge graphs, which combined the semantic and structural information. Also, this paper proposed the use of strong attribute constraint based on adaptive attribute selection, which could adaptively generate the most effective attribute category and weight, to improve the performance of entity alignment. Experiments on two realistic datasets showed that, compared with traditional methods, the precision of the proposed method was improved by 11%.