Conflict-aware multilingual knowledge graph completion

Abstract

Knowledge graph completion (KGC), a task that aims at predicting missing links with existing information inside a knowledge graph (KG), has emerged as a popular research area in recent years. While many existing works have demonstrated effectiveness on a single knowledge graph KGC, limited effort has been devoted to exploring the potentially complementary nature of multiple KGs. In this work, we proposed a novel method called CA-MKGC (Conflict-aware Multilingual Knowledge Graph Completion) for multiple knowledge graph completion (MKGC), aiming to alleviate the sparseness of a single knowledge graph by leveraging information from other knowledge graphs. We designed an intra-KG graph convolutional network encoder that regards the seed alignments between KGs as edges for intra-KG message propagation to model all KGs in a unified model while also adopting an iterative mechanism to progressively incorporate newly predicted alignments along with the newly inferred facts into the learning process. Additionally, we employed an active learning mechanism and a greedy approximation to a semi-constrained optimization problem to focus on learning the structural prior knowledge that is difficult to learn in semantic space, limiting the propagation of error in the iterative training process. Experimental results on multilingual KG datasets demonstrated that our method achieved state-of-the-art results.