Abstract:
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 approach finds multiple \textit{graph path patterns} that connect similar source entities through the given relation, and looks for pattern matches starting from the query source.
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.
