论文标题
实体对齐的关系感知的邻里匹配模型
Relation-Aware Neighborhood Matching Model for Entity Alignment
论文作者
论文摘要
旨在将实体与不同知识图（kgs）相同含义联系起来的实体一致性是知识融合的重要步骤。现有的研究重点是学习实体的学习嵌入，通过利用KGS的结构信息进行实体一致性。这些方法可以从相邻节点汇总信息，但也可能带来邻居的噪音。最近，一些研究人员试图比较成对的相邻节点，以增强实体对齐。但是，他们忽略了实体之间的关系，这对于邻里匹配也很重要。此外，现有方法更少注意实体一致性与关系一致性之间的积极相互作用。为了解决这些问题，我们提出了一种新颖的关系感知的邻里匹配模型，名为RNM，以实体对齐。具体而言，我们建议利用邻域匹配来增强实体对齐。除了比较匹配邻域时的邻居节点外，我们还尝试探索连接关系中的有用信息。此外，迭代框架旨在以半监督的方式利用实体一致性和关系对准之间的积极相互作用。三个现实世界数据集的实验结果表明，所提出的模型RNM的性能要比最新方法更好。
Entity alignment which aims at linking entities with the same meaning from different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing research focused on learning embeddings of entities by utilizing structural information of KGs for entity alignment. These methods can aggregate information from neighboring nodes but may also bring noise from neighbors. Most recently, several researchers attempted to compare neighboring nodes in pairs to enhance the entity alignment. However, they ignored the relations between entities which are also important for neighborhood matching. In addition, existing methods paid less attention to the positive interactions between the entity alignment and the relation alignment. To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. Specifically, we propose to utilize the neighborhood matching to enhance the entity alignment. Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations. Moreover, an iterative framework is designed to leverage the positive interactions between the entity alignment and the relation alignment in a semi-supervised manner. Experimental results on three real-world datasets demonstrate that the proposed model RNM performs better than state-of-the-art methods.
