  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
