Neurosymbolic Methods for Rule Mining
<|reference_start|>Neurosymbolic Methods for Rule Mining: 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.<|reference_end|>
@article{lawrynowicz2024neurosymbolic,
    title={Neurosymbolic Methods for Rule Mining},
    author={Agnieszka Lawrynowicz, Luis Galarraga, Mehwish Alam, Berenice Jaulmes,
  Vaclav Zeman, Tomas Kliegr},
    journal={arXiv preprint arXiv:2408.05773},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05773},
    primaryClass={cs.AI}
}