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Presents Moose, a neuro-symbolic method that compiles OWL 2 EL ontologies into Sentential Decision Diagrams for differentiable weighted model counting, enabling latent concept learning under partial supervision and providing the first reasoning-shortcut analysis in an OWL EL setting.
This paper analyzes reasoning shortcuts in neurosymbolic systems through symmetry and automorphism groups, reveals flaws in a prior framework, proves complexity-theoretic results, and empirically validates the corrected theory on benchmarks like CLE4EVR and rsbench.