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Knowledge graphs (KGs) play a crucial role in many applications, such asquestion answering, but incompleteness is an urgent issue for their broadapplication. Much research in knowledge graph completion (KGC) has beenperformed to resolve this issue. The methods of KGC can be classified into twomajor categories: rule-based reasoning and embedding-based reasoning. Theformer has high accuracy and good interpretability, but a major challenge is toobtain effective rules on large-scale KGs. The latter has good efficiency andscalability, but it relies heavily on data richness and cannot fully use domainknowledge in the form of logical rules. We propose a novel method that injectsrules and learns representations iteratively to take full advantage of rulesand embeddings. Specifically, we model the conclusions of rule groundings as0-1 variables and use a rule confidence regularizer to remove the uncertaintyof the conclusions. The proposed approach has the following advantages: 1) Itcombines the benefits of both rules and knowledge graph embeddings (KGEs) andachieves a good balance between efficiency and scalability. 2) It uses aniterative method to continuously improve KGEs and remove incorrect ruleconclusions. Evaluations on two public datasets show that our methodoutperforms the current state-of-the-art methods, improving performance by2.7\% and 4.3\% in mean reciprocal rank (MRR).

Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules