Neurosymbolic Methods for Rule Mining
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917746250350592 |
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| author | Lawrynowicz, Agnieszka Galarraga, Luis Alam, Mehwish Jaulmes, Berenice Zeman, Vaclav Kliegr, Tomas |
| author_facet | Lawrynowicz, Agnieszka Galarraga, Luis Alam, Mehwish Jaulmes, Berenice Zeman, Vaclav Kliegr, Tomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neurosymbolic Methods for Rule Mining Lawrynowicz, Agnieszka Galarraga, Luis Alam, Mehwish Jaulmes, Berenice Zeman, Vaclav Kliegr, Tomas Artificial Intelligence 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. |
| title | Neurosymbolic Methods for Rule Mining |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2408.05773 |