Neurosymbolic Methods for Rule Mining

TL;DR

This paper reviews neurosymbolic methods for rule mining, covering techniques like deep learning integration, embeddings, and large language models, and categorizes various rule mining methodologies.

Contribution

It provides a comprehensive overview of neurosymbolic approaches and categorizes existing rule mining methodologies, highlighting recent advances with large language models.

Findings

Neurosymbolic methods effectively combine neural networks with symbolic rules.

Embedding techniques enhance rule learning capabilities.

Large language models are increasingly applied in rule mining.

Abstract

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.