Rule-enhanced iterative complementation for knowledge graph reasoning

Knowledge graph (KG) reasoning aims to infer missing valid triples from observed triples, thereby improving the semantics of the whole KG. The general KG reasoning involves rule-based and embedding-based methods. The former can provide an interpretable reasoning process but has low efficiency, while the latter is the converse. Therefore, some hybrid methods have been proposed, but there are still two challenges: the completeness of rule learning and the determination of hidden triples. To address these challenges, this paper proposes a rule-enhanced iterative complementation (Rule-IC) method, which involves three components: rule learning, an embedding learner and a triple discriminator. Such an iterative process enriches the semantics of KG and further increases the completeness of rule learning to generate hidden triples. In order to precisely determine the validity of hidden triples, a multi-relational graph convolutional network (GCN) with attentive message passing is introduced as a triple discriminator. The embedding learner for KG reasoning and the GCN discriminator complement each other by valid hidden triples. In addition, the performance of these three components improves overall during the iterative process. Experimental results show that most evaluation metrics of Rule-IC are better than those of several baselines on four common KGs. Furthermore, it is scalable and can be extended to all KG embedding models theoretically.