Neurosymbolic Methods for Rule Mining
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 43 references that are in the map. First posted on arXiv 11 Aug 2024.
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10 of 43 references.
References · 10
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
- 2015Holographic Embeddings of Knowledge Graphscited by 181
- 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 173
- 2020RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphscited by 117
- 2023Large Language Models can Learn Rulescited by 66
- 2016TensorLog: A Differentiable Deductive Databasecited by 29
- 2023ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoningcited by 26
- 2016SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptionscited by 18
- 2021SAFRAN: An interpretable, rule-based link prediction method outperforming embedding modelscited by 15
- 2015From Data Fusion to Knowledge Fusioncited by 10
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Citing papers per year: 2026: 1.
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