Summary of Neurosymbolic Methods For Rule Mining, by Agnieszka Lawrynowicz et al.
Neurosymbolic Methods for Rule Mining
by Agnieszka Lawrynowicz, Luis Galarraga, Mehwish Alam, Berenice Jaulmes, Vaclav Zeman, Tomas Kliegr
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of rule mining by providing a comprehensive overview of various methodologies. The authors start by explaining the importance of measuring rule quality, followed by an exploration of different approaches to rule mining. These include inductive logic programming, path sampling and generalization, and linear programming. Additionally, the chapter delves into neurosymbolic methods that combine deep learning with rules, using embeddings for rule learning, and applying large language models in rule learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rule mining is a crucial problem in artificial intelligence that involves discovering meaningful patterns or rules from data. This paper provides an overview of different methodologies used to solve this challenge. The authors start by explaining why measuring rule quality is important, then discuss various approaches including logic programming and linear programming. They also explore the integration of deep learning with rules and the use of large language models in rule learning. |
Keywords
» Artificial intelligence » Deep learning » Generalization
