Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years. However, the predictive power of KGC methods is often limited by the completeness of the existing knowledge graphs from different sources and languages. In monolingual and multilingual settings, KGs are potentially complementary to each other. In this paper, we study the problem of multi-KG completion, where we focus on maximizing the collective knowledge from different KGs to alleviate the incompleteness of individual KGs. Specifically, we propose a novel method called CKGC-CKD that uses relation-aware graph convolutional network encoder models on both individual KGs and a large fused KG in which seed alignments between KGs are regarded as edges for message propagation. An additional mutual knowledge distillation mechanism is also employed to maximize the knowledge transfer between the models of "global" fused KG and the "local" individual KGs. Experimental results on multilingual datasets have shown that our method outperforms all state-of-the-art models in the KGC task.

翻译：知识图谱补全（KGC）作为基于知识图谱（KG）内部现有关系数据预测缺失信息的任务，近年来受到广泛关注。然而，KGC方法的预测能力往往受限于不同来源和语言现有关知识图谱的完备性。在单语和多语场景中，知识图谱具有潜在互补性。本文研究多知识图谱补全问题，重点通过最大化不同知识图谱的集体知识来缓解单个知识图谱的不完备性。具体而言，我们提出名为CKGC-CKD的新方法，该方法在单个知识图谱以及大型融合知识图谱上使用关系感知图卷积网络编码器模型，其中知识图谱间的种子对齐被视为消息传播的边。同时引入互知识蒸馏机制，最大化"全局"融合知识图谱与"局部"单个知识图谱模型之间的知识迁移。多语言数据集上的实验结果表明，该方法在KGC任务中优于所有现有最优模型。