让知识图谱学会推理捷径，提升小样本概念学习能力
Moose: Latent concept learning with reasoning-shortcut awareness in $\mathcal{EL}^{++}$
- 将EL++本体编译为可微分的命题决策图，支持部分监督下的隐式概念学习
- 在MNIST-with-ontology和Pizzaïolo上超越传统神经符号方法，准确率提升12%以上
- 首次在OWL EL场景中分析推理捷径，适合医疗、生物等知识密集型领域研究者
OWL 2 EL 是基因本体和SNOMED CT等大型生产级本体的核心。现有神经符号学习方法多基于命题理论或Datalog，且未研究推理捷径（RS）的影响。本文提出Moose，将$ \mathcal{EL}^{++}$ TBox与有限ABox编译为句法决策图（SDD），SDD作为可微加权模型计数层，并在声明的穷尽族外添加闭包谓词，以克服$ \mathcal{EL}^{++}$在部分监督下的表达力不足。我们证明了终止性、正确性、完备性及多项式中间规模，并在Lean中验证。进一步定义了首个在OWL EL本体上的部分监督隐式概念学习任务——从观察到的ABox断言中学习个体级分类器。在MNIST-with-ontology和Pizzaïolo上，Moose优于命题神经符号、模糊逻辑及本体嵌入基线，并首次在OWL EL设置中开展推理捷径分析。
原文摘要 · Abstract (English)
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.
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