Neurosymbolic Methods for Rule Mining
It provides a comprehensive overview for researchers in AI and data mining, but is incremental as it synthesizes existing methods rather than introducing new ones.
This chapter surveys existing rule mining methodologies and explores neurosymbolic approaches that integrate deep learning, embeddings, and large language models with rule learning.
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.