论文标题
一种简单的方法，用于知识库中基于案例推理的方法
A Simple Approach to Case-Based Reasoning in Knowledge Bases
论文作者
论文摘要
我们在知识图（kgs）中提出了一种令人惊讶的简单而准确的推理方法，该方法需要\ emph {no triench}，并且让人联想到经典人工智能（AI）中基于案例的推理。考虑给定源实体和二进制关系找到目标实体的任务。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模式}来得出每个查询的清晰逻辑规则。使用我们的方法，我们获得了Nell-995和FB-122上的所有先前模型的新最先进的精度。我们还证明，我们的模型在低数据设置中具有鲁棒性，最近提出的元学习方法优于最近的模型
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
