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.

本文介绍了一种名为 CKGC-CKD 的新方法，通过使用关系感知图卷积网络编码模型在集成的知识图谱和各自的知识图谱上进行训练，以最大程度地利用来自不同知识图谱的集体知识，从而缓解了个体知识图谱的不完整性，并通过相互知识蒸馏机制来进行知识传递，在多语言数据集上取得了比所有先进模型更好的知识图谱完成结果。