We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. Experimental results show that our proposed method can effectively embed unseen components and outperforms models that consider inductive settings for KGs and baselines that directly use conventional KG embedding methods.

翻译：我们研究知识外推问题,以在联盟环境中嵌入新的组成部分(即实体和关系),这些组成部分是新出现的知识图表(KGs)产生的。在此问题上,对现有KG进行训练的模型需要将新兴KG与隐形实体和关系嵌入。为解决这一问题,我们引入了元学习设置,在现有KG中抽查了一套任务,以模拟新兴KG的链接预测任务。根据抽样任务,我们元培训了一个图形神经网络框架,可以基于结构信息和输出嵌入来构建未知组件的特征。实验结果显示,我们拟议的方法可以有效地嵌入隐形组件和外形模型,这些模型可以考虑KGs和直接使用常规KG嵌入方法的基线的缩入环境。