# A Simple Approach to Case-Based Reasoning in Knowledge Bases

arXiv: https://arxiv.org/abs/2006.14198 · HTML: https://mlsanity.com/paper/2006.14198/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021)
- [Knowledge Base Question Answering by Case-based Reasoning over Subgraphs](https://mlsanity.com/paper/2202.10610/) (2022)
- [Retrieval-Enhanced Machine Learning: Synthesis and Opportunities](https://mlsanity.com/paper/2407.12982/) (2024)
- [Case-based Reasoning for Better Generalization in Textual Reinforcement Learning](https://mlsanity.com/paper/2110.08470/) (2021)
- [Case-Based Reasoning with Language Models for Classification of Logical Fallacies](https://mlsanity.com/paper/2301.11879/) (2023)
- [Data-to-text Generation by Splicing Together Nearest Neighbors](https://mlsanity.com/paper/2101.08248/) (2021)
- [Machine Reading Comprehension using Case-based Reasoning](https://mlsanity.com/paper/2305.14815/) (2023)
- [Event Prediction using Case-Based Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2309.12423/) (2023)

## Abstract (arXiv)

We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires 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 textitgraph 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