A Simple Approach to Case-Based Reasoning in Knowledge BasesDownload PDF

Published: 01 May 2020, Last Modified: 22 Oct 2023AKBC 2020Readers: Everyone
Keywords: case based reasoning, non-parametric reasoning, knowledge base completion
TL;DR: Learn to answer a query about an entity by gathering reasoning paths from other similar entities in the Knowledge Base
Subject Areas: Knowledge Representation, Semantic Web and Search, QuestionAnswering and Reasoning, Relational AI
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.
Archival Status: Archival
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2006.14198/code)
8 Replies