论文标题
基于理论规则的知识图推理通过连接性依赖发现
Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
论文作者
论文摘要
从知识图中发现精确且可解释的规则被认为是一个必不可少的挑战，可以改善许多下游任务的性能，甚至提供新的方法来了解一些自然语言处理研究主题。在本文中，我们提出了一种基于规则的知识图推理的基本理论，该理论基于图中的连通性依赖性通过多种规则类型捕获。这是在知识图中首次考虑其中一些规则类型。基于这些规则类型，我们的理论可以为未知的三元组提供精确的解释。然后，我们通过所谓的统治模型来实现我们的理论。结果表明，我们的统治模型不仅提供了解释新三元的精确规则，而且还可以在一个基准知识图完成任务上实现最先进的表演，并且在其他任务上具有竞争力。
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.
