Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics. In this paper, we present a fundamental theory for rule-based knowledge graph reasoning, based on which the connectivity dependencies in the graph are captured via multiple rule types. It is the first time for some of these rule types in a knowledge graph to be considered. Based on these rule types, our theory can provide precise interpretations to unknown triples. Then, we implement our theory by what we call the RuleDict model. Results show that our RuleDict model not only provides precise rules to interpret new triples, but also achieves state-of-the-art performances on one benchmark knowledge graph completion task, and is competitive on other tasks.
翻译:从知识图表中发现准确和可解释的规则被视为一项基本挑战,它可以改善许多下游任务的业绩,甚至提供新的方法处理某些自然语言处理研究课题。在本文中,我们提出了基于规则的知识图表推理的基本理论,据此通过多种规则类型来捕捉图中的连接依赖性。这是首次在知识图表中考虑其中一些规则类型。根据这些规则类型,我们的理论可以向未知的三倍提供精确的解释。然后,我们用我们所谓的规则数据模型来实施我们的理论。结果显示,我们的规则数据模型不仅为解释新的三重任务提供了精确的规则,而且还在一个基准知识图表完成任务上实现了最先进的业绩,并在其他任务上具有竞争力。