Taxonomic Networks: A Representation for Neuro-Symbolic Pairing
Proceedings of the 2nd International Conference on Neuro-symbolic Systems
2025
Abstract
We introduce the concept of a neuro-symbolic pair -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present 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.
BibTeX
@inproceedings{wang-neus-2025,
title = {Taxonomic Networks: A Representation for Neuro-Symbolic Pairing},
author = {Wang, Zekun and Haarer, Ethan and Barari, Nicki and MacLellan, Christopher J.},
booktitle = {Proceedings of the 2nd International Conference on Neuro-symbolic Systems},
pages = {459-471},
year = {2025},
}