Collective Knowledge Graph Completion with Mutual Knowledge Distillation
Weihang Zhang Ovidiu Serban Jiahao Sun Yi-ke Guo
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.