We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI). Consider the task of finding a target entity given a source entity and a binary relation. Our non-parametric approach derives crisp logical rules for each query by finding multiple \textit{graph path patterns} that connect similar source entities through the given relation. Using our method, we obtain new state-of-the-art accuracy, outperforming all previous models, on NELL-995 and FB-122. We also demonstrate that our model is robust in low data settings, outperforming recently proposed meta-learning approaches

中文翻译：

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知识库中基于案例推理的简单方法

我们提出了一种在知识图谱 (KG) 中进行推理的令人惊讶的简单而准确的方法，它需要 \emph {no training}，并且让人想起经典人工智能 (AI) 中的基于案例的推理。考虑在给定源实体和二元关系的情况下查找目标实体的任务。我们的非参数方法通过查找通过给定关系连接相似源实体的多个 \textit{graph path patterns} 为每个查询推导出清晰的逻辑规则。使用我们的方法，我们在 NELL-995 和 FB-122 上获得了新的最先进的准确度，优于所有以前的模型。我们还证明了我们的模型在低数据设置下是稳健的，优于最近提出的元学习方法