中圖分類號： TP393 文獻標識碼： A DOI：10.16157/j.issn.0258-7998.222717 中文引用格式： 汪浣沙，黃瑞陽，宋旭暉，等. 基于動態(tài)圖注意力聚合多跳鄰域的實體對齊[J].電子技術(shù)應用，2022，48(11)：51-56. 英文引用格式： Wang Huansha，Huang Ruiyang，Song Xuhui，et al. Entity alignment based on dynamic graph attention aggregation in multi-hop neighborhood[J]. Application of Electronic Technique，2022，48(11)：51-56.
Entity alignment based on dynamic graph attention aggregation in multi-hop neighborhood
Wang Huansha1，2，Huang Ruiyang1，2，Song Xuhui3，Yu Shiyuan3，Hu Nan3
1.National Digital Switching System Engineering & Technological R&D Center，Zhengzhou 450002，China； 2.Information Engineering University，Zhengzhou 450002，China；3.Software College，Zhengzhou University，Zhengzhou 450001，China
Abstract： Entity alignment is an important technical method to realize the fusion of knowledge bases from different sources. It is widely used in the fields of knowledge graph and knowledge completion. The existing entity alignment models based on graph attention mostly use static graph attention network and ignore the semantic information in entity attributes, resulting in the problems of limited attention, difficult fitting and insufficient expression ability of the model. To solve these problems, this paper studies the entity alignment method based on the structure modeling of dynamic graph attention. Firstly, the single hop node representation of the target entity is modeled by GCN. Secondly, the multi hop node attention coefficient is obtained and entity modeled by using the dynamic graph attention network, and then the single hop and multi hop node information output by GCN and dynamic graph attention layer is aggregated by layer-wise gating network. Finally, the entity attribute semantic extracted by external knowledge pre training natural language model is embedded and concatenated to calculate similarity. This method has been improved in three types of cross language datasets of DBP15K, which proves the effectiveness of applying dynamic graph attention network and integrating entity attribute semantics in improving entity representation ability.
Key words : dynamic GAT；graph convolution network；entity alignment；knowledge graph；representation learning
