# Neurosymbolic Methods for Rule Mining

arXiv: https://arxiv.org/abs/2408.05773 · HTML: https://mlsanity.com/paper/2408.05773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 11 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs](https://mlsanity.com/paper/1911.00055/) (2019)
- [RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2010.04029/) (2020)
- [Large Language Models can Learn Rules](https://mlsanity.com/paper/2310.07064/) (2023)
- [TensorLog: A Differentiable Deductive Database](https://mlsanity.com/paper/1605.06523/) (2016)
- [ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning](https://mlsanity.com/paper/2309.01538/) (2023)
- [SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions](https://mlsanity.com/paper/1604.04835/) (2016)
- [SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models](https://mlsanity.com/paper/2109.08002/) (2021)
- [From Data Fusion to Knowledge Fusion](https://mlsanity.com/paper/1503.00302/) (2015)

## Built on by (most cited first, 1 of 1)

- [SoK: Formal Methods for Fact-Checking and Information Integrity](https://mlsanity.com/paper/2609.23239/) (2026)

## Abstract (arXiv)

In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining methodologies, categorized into three groups: inductive logic programming, path sampling and generalization, and linear programming. Following this, we delve into neurosymbolic methods, covering topics such as the integration of deep learning with rules, the use of embeddings for rule learning, and the application of large language models in rule learning.