Utilizing Concept Ontologies for Designing Neural Network-Based Classifiers

Deep neural networks have achieved substantial success in image, text, and signal analysis, but their advantage is less consistent for heterogeneous tabular data, where tree-based ensemble methods often remain strong baselines. This study proposes CANON (Cross-Attention Neuro-symbolic Ontology Network), a neuro-symbolic architecture that integrates a hierarchical a priori concept ontology with a modular mixture-of-experts mechanism. CANON is designed to combine data-driven representation learning with explicit domain structure and to reduce the influence of irrelevant or weakly informative features. The architecture was evaluated on two clinical tabular cohorts—848 patients with ANCA-associated vasculitis described by 142 features, and 200 patients assessed for coronary artery stenosis described by 593 features—using stratified 8-fold cross-validation, and was compared with tree-based ensembles, dedicated tabular deep learning models, and classical neural architectures. CANON achieved the highest mean AUC on both cohorts (0.9426 and 0.9484). Two findings are reported. First, CANON significantly outperformed in AUC all four non-tree baselines included in the paired statistical analysis: all eight paired comparisons against GaussianNB, FT-Transformer, TabNet and LSTM were significant on both cohorts and favored CANON in 8 of 8 folds, with FT-Transformer and TabNet tuned separately for each cohort under an identical budget of 20 Optuna trials each. Second, CANON performed comparably to the tree-based ensembles, significantly outperforming Random Forest on the coronary artery stenosis cohort (Δ=+0.124, p=0.004), while the remaining comparisons against Random Forest and XGBoost did not reach significance. An ablation study comprising 40 cross-validation folds per variant shows that the semantic organization of features into concepts and the non-linear feature tokenizer contribute measurably to the harder, higher-dimensional cohort, whereas the directional fusion mechanism does not improve predictive performance and is retained for its interpretability role. These findings indicate that ontology-guided neural architectures can provide a competitive and interpretable basis for clinical decision support and may also be useful in other regulated domains in which predictive models must remain consistent with domain-specific knowledge.