A Simple Approach to Case-Based Reasoning in Knowledge Bases

• URL: http://arxiv.org/abs/2006.14198v2
• Date: Sun, 19 Jul 2020 01:26:35 GMT
• Title: A Simple Approach to Case-Based Reasoning in Knowledge Bases
• Authors: Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum
• Abstract summary: We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires emphno 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 textitgraph path patterns that connect similar source entities through the given relation.
• Score: 56.661396189466664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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