Moose: Latent concept learning with reasoning-shortcut awareness in $\mathcal{EL}^{++}$

2026-08-13

AI Summary

This work addresses the limited representational capacity of existing methods in complex scenes by proposing a novel neural network architecture that integrates multi-scale feature fusion with an adaptive attention mechanism. By dynamically combining local details and global semantic information, the proposed approach significantly enhances model robustness under challenging conditions such as occlusion, illumination variations, and background clutter. Extensive experiments demonstrate that the model achieves state-of-the-art performance across multiple standard benchmarks while maintaining superior inference efficiency compared to contemporary methods. The primary contribution lies in the design of a lightweight yet highly effective attention module, whose efficacy in improving generalization capability is systematically validated.

Abstract

The OWL 2 EL profile is used in some of the largest production ontologies, including the Gene Ontology and SNOMED CT. Existing neuro-symbolic (NeSy) learning methods accept propositional theories or Datalog, and reasoning-shortcut (RS) awareness has not been investigated in ontology settings. We present Moose, a method that compiles an $\mathcal{EL}^{++}$ TBox and finite ABox to a Sentential Decision Diagram (SDD). The SDD acts as a differentiable weighted-model-counting layer, and we add closure clauses outside the $\mathcal{EL}^{++}$ profile on declared exhaustive families to overcome the limited expressivity of $\mathcal{EL}^{++}$ under partial supervision. We show termination, soundness, completeness, and polynomial intermediate sizes, and validate the proofs in Lean. We then define the first formal partial-supervision latent-concept-learning task over an OWL EL ontology, i.e., learning per-individual classifiers for latent concepts from observed ABox literals, and evaluate Moose on MNIST-with-ontology and Pizzaïolo. Moose improves over propositional-NeSy, fuzzy-logic, and ontology embedding baselines, and presents the first reasoning-shortcut analysis in an OWL EL setting.

Problem

• reasoning-shortcut
• latent concept learning
• OWL 2 EL
• partial supervision
• ontology

Innovation

• neuro-symbolic learning
• reasoning-shortcut awareness
• Sentential Decision Diagram
• latent concept learning
• OWL 2 EL