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Knowledge graph completion (KGC), the task of predicting missing informationbased on the existing relational data inside a knowledge graph (KG), has drawnsignificant attention in recent years. However, the predictive power of KGCmethods is often limited by the completeness of the existing knowledge graphsfrom different sources and languages. In monolingual and multilingual settings,KGs are potentially complementary to each other. In this paper, we study theproblem of multi-KG completion, where we focus on maximizing the collectiveknowledge from different KGs to alleviate the incompleteness of individual KGs.Specifically, we propose a novel method called CKGC-CKD that usesrelation-aware graph convolutional network encoder models on both individualKGs and a large fused KG in which seed alignments between KGs are regarded asedges for message propagation. An additional mutual knowledge distillationmechanism is also employed to maximize the knowledge transfer between themodels of "global" fused KG and the "local" individual KGs. Experimentalresults on multilingual datasets have shown that our method outperforms allstate-of-the-art models in the KGC task.

Collective Knowledge Graph Completion with Mutual Knowledge Distillation