Moose compiles OWL 2 EL ontologies into a Lean-verified differentiable weighted-model-counting layer and uses it to learn latent concept labels under partial supervision, beating propositional neuro-symbolic baselines on relational and role-chain MNIST regimes.
The Shape of $\mathcal{EL}$ Proofs: A Tale of Three Calculi (Extended Version)
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Consequence-based reasoning can be used to construct proofs that explain entailments of description logic (DL) ontologies. In the literature, one can find multiple consequence-based calculi for reasoning in the $\mathcal{EL}$ family of DLs, each of which gives rise to proofs of different shapes. Here, we study three such calculi and the proofs they produce on a benchmark based on the OWL Reasoner Evaluation. The calculi are implemented using a translation into existential rules with stratified negation, which had already been demonstrated to be effective for the calculus of the ELK reasoner. We then use the rule engine NEMO to evaluate the rules and obtain traces of the rule execution. After translating these traces back into DL proofs, we compare them on several metrics that reflect different aspects of their complexity.
fields
cs.AI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Moose: Latent concept learning with reasoning-shortcut awareness in $\mathcal{EL}^{++}$
Moose compiles OWL 2 EL ontologies into a Lean-verified differentiable weighted-model-counting layer and uses it to learn latent concept labels under partial supervision, beating propositional neuro-symbolic baselines on relational and role-chain MNIST regimes.