摘要
在实现不同来源的图谱数据融合过程中,实体对齐是关键的步骤,其目的在于确定不同图谱间等价的实体对.现有实体对齐方法大多基于图嵌入方式,通过考虑图谱的结构和属性信息进行对齐,但并未很好处理二者之间的交互关系,同时忽略对关系及多阶邻居信息的利用.为解决上述问题,提出一种融合结构与属性注意力机制模型(fused structural and attribute attention mechanism model,FSAAM)的实体对齐方法.该模型首先根据图谱数据特征划分为属性和结构通道数据,其次使用属性注意力机制实现对属性信息的学习,在实现对结构信息的学习中增加对关系信息的学习,利用图注意力机制寻找对于实体对齐有益的邻居特征,引入Transformer编码器更好的关联实体之间的信息,并通过Highway网络减少可能学习到噪声信息的影响,最后对学习到的结构通道和属性通道信息的相似度矩阵利用LS-SVM网络,得到集成相似度矩阵从而实现实体对齐.所提模型在公开数据集DBP15K的3个子数据集上进行验证.实验结果表明,相较于基线模型中效果最好的结果,其Hits@1分别提高了2.7%,4.3%和1.7%,且Hits@10和MRR也均有提升,表明本模型能够有效提高实体对齐的准确性.
For fusing map data from different sources,entity alignment is a key step,and its purpose is to determine equivalent entity pairs between different maps.Most of the existing entity alignment methods are based on graph embedding,which aligns by considering the structure and attribute information of the graph.However,they do not handle the interactive relationship between the two well and ignore the use of relationships and multi-order neighbor information.To solve the above problems,this study proposes an entity alignment method based on the fused structural and attribute attention mechanism model(FSAAM).The model first divides the graph data characteristics into attribute and structural channel data and then uses the attribute attention mechanism to learn attribute information.It adds the learning of relationship information to that of structural information and uses the graph attention mechanism to find the entities aligned with beneficial neighbor features.The Transformer encoder is introduced to better correlate information between entities,and the Highway network is utilized to reduce the impact of noise information that may be learned.Finally,the model applies the LS-SVM network to the similarity matrix of the learned structural channel and the attribute channel information,obtaining the integrated similarity matrix to achieve entity alignment.The proposed model is verified on three sub-datasets of the public data set DBP15K.Experimental results show that compared with the best results in the baseline model,its Hits@1 has increased by 2.7%,4.3%,and 1.7% respectively,and Hits@10 and MRR have also improved,indicating that this model can effectively improve the alignment accuracy of entities.
作者
李忠阳
王淑营
蒋敏
LI Zhong-Yang;WANG Shu-Ying;JIANG Min(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)
出处
《计算机系统应用》
2024年第6期58-69,共12页
Computer Systems & Applications
基金
国家重点研发计划(2020YFB1708000)
四川省重大科技专项(2022ZDZX0003)。
