# Moose: Latent concept learning with reasoning-shortcut awareness in EL++

arXiv: https://arxiv.org/abs/2608.12961 · HTML: https://mlsanity.com/paper/2608.12961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts](https://mlsanity.com/paper/2402.12240/) (2024)
- [To Neuro-Symbolic Classification and Beyond by Compiling Description Logic Ontologies to Probabilistic Circuits](https://mlsanity.com/paper/2601.14894/) (2026)
- [VEL: A Formally Verified Reasoner for OWL2 EL Profile](https://mlsanity.com/paper/2412.08739/) (2024)
- [The Shape of EL Proofs: A Tale of Three Calculi (Extended Version)](https://mlsanity.com/paper/2507.21851/) (2025)

## Abstract (arXiv)

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 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 EL++ profile on declared exhaustive families to overcome the limited expressivity of 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 Pizza"iolo. Moose improves over propositional-NeSy, fuzzy-logic, and ontology embedding baselines, and presents the first reasoning-shortcut analysis in an OWL EL setting.