Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7\% and 4.3\% in mean reciprocal rank (MRR).
翻译:知识图表(KGs)在许多应用中发挥着关键作用,例如回答问题,但不完整是其广泛应用的一个紧迫问题。已经对知识图的完成(KGC)进行了大量研究,以解决这一问题。KGC的方法可以分为两大类:基于规则的推理和基于嵌入的推理。前者具有很高的准确性和良好的解释性,但一项重大挑战是获得关于大规模KGs的有效规则。后者具有良好的效率和可缩放性,但后者在很大程度上依赖于数据丰富性,不能以逻辑规则的形式充分利用域知识。我们提出了一种新的方法,为充分利用规则和嵌入的嵌入规则并反复学习演示。具体地说,我们将规则基础的结论作为0-1变量和基于嵌入的推理的推理进行模型,并使用规则的固定信心来消除结论的不确定性。拟议方法有以下优点:(1) 将规则和知识图嵌入(KGEGes)的效益结合起来,无法在效率和可缩放性之间实现良好的平衡。我们用一种迭代方法来注入规则,并学习演示演示演示各种规则和嵌入的相互性规则。我们目前采用的方法改进了4.M的正方标准,通过改进了正方标准,从而改进了对等化的进度改进了正方的进度。