Taxonomic Networks: A Representation for Neuro-Symbolic Pairing
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913867621203968 |
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| author | Wang, Zekun Haarer, Ethan L. Barari, Nicki MacLellan, Christopher J. |
| author_facet | Wang, Zekun Haarer, Ethan L. Barari, Nicki MacLellan, Christopher J. |
| contents | We introduce the concept of a \textbf{neuro-symbolic pair} -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present \textbf{taxonomic networks}, a type of discrimination network in which nodes represent hierarchically organized taxonomic concepts. Using this representation, we construct a novel neuro-symbolic pair and evaluate its performance. We show that our symbolic method learns taxonomic nets more efficiently with less data and compute, while the neural method finds higher-accuracy taxonomic nets when provided with greater resources. As a neuro-symbolic pair, these approaches can be used interchangeably based on situational needs, with seamless translation between them when necessary. This work lays the foundation for future systems that more fundamentally integrate neural and symbolic computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24601 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Taxonomic Networks: A Representation for Neuro-Symbolic Pairing Wang, Zekun Haarer, Ethan L. Barari, Nicki MacLellan, Christopher J. Artificial Intelligence We introduce the concept of a \textbf{neuro-symbolic pair} -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present \textbf{taxonomic networks}, a type of discrimination network in which nodes represent hierarchically organized taxonomic concepts. Using this representation, we construct a novel neuro-symbolic pair and evaluate its performance. We show that our symbolic method learns taxonomic nets more efficiently with less data and compute, while the neural method finds higher-accuracy taxonomic nets when provided with greater resources. As a neuro-symbolic pair, these approaches can be used interchangeably based on situational needs, with seamless translation between them when necessary. This work lays the foundation for future systems that more fundamentally integrate neural and symbolic computation. |
| title | Taxonomic Networks: A Representation for Neuro-Symbolic Pairing |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.24601 |