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arXiv · 2006.14198

A Simple Approach to Case-Based Reasoning in Knowledge Bases

Rajarshi Das·Ameya Godbole·Shehzaad Dhuliawala·Manzil Zaheer·Andrew McCallum
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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 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

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Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum. 2020-06-25. A Simple Approach to Case-Based Reasoning in Knowledge Bases. https://arxiv.org/abs/2006.14198

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