论文标题
在知识图中的节点属性完成，并具有多关系传播
Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
论文作者
论文摘要
有关知识图完成的现有文献主要集中在链接预测任务上。但是，知识图有一个附加的不完整问题：它们的节点具有数值属性，其值通常丢失。我们的方法被称为MRAP，通过在知识图的多关系结构上传播信息来强化缺失属性的值。它采用回归函数来预测另一个节点属性，这取决于节点与属性的类型之间的关系。传播机制在消息传递方案中迭代运行，该方案在每次迭代中收集预测并更新节点属性的值。两个基准数据集的实验显示了我们方法的有效性。
The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, denoted as MrAP, imputes the values of missing attributes by propagating information across the multi-relational structure of a knowledge graph. It employs regression functions for predicting one node attribute from another depending on the relationship between the nodes and the type of the attributes. The propagation mechanism operates iteratively in a message passing scheme that collects the predictions at every iteration and updates the value of the node attributes. Experiments over two benchmark datasets show the effectiveness of our approach.
