Encoding Higher-Order Logic in Spatio-Temporal Hypergraphs for Neuro-Symbolic Learning

This work integrates Monadic Second-Order (MSO) logic into Spatio-Temporal Heterogeneous Hypergraphs (STHH) to advance Neuro-Symbolic AI.By bridging higher-ordered symbolic logic with neural computations, STHH offers a novel framework for knowledge representation and learning.Evaluations on a custom agricultural dataset show that the proposed STHH outperforms state-of-the-art hypergraph models across F1-score, accuracy, and AUC metrics.Despite challenges such as limited standardized datasets, this study underscores the potential of integrating higher-ordered symbolic logic into neural systems to achieve robust and interpretable AI.