提出神经推理器EBR，让知识库检索更抗错、更鲁棒。
Neural Reasoning for Robust Instance Retrieval in $\mathcal{SHOIQ}$
- 用嵌入近似符号推理，仅需检索原子概念和存在限制实例
- 在含缺失/错误数据时仍保持准确，优于现有推理器
- 适合真实世界知识库的可解释分类任务
概念学习利用描述逻辑公理形式的背景知识，从知识库中学习可解释的分类模型。尽管神经符号概念学习取得进展，但多数方法难以部署于真实知识库，原因在于依赖对不一致性和错误数据不鲁棒的描述逻辑推理器。本文提出新型神经推理器EBR，其基于嵌入近似符号推理结果。实验表明，EBR仅需检索原子概念和存在限制的实例，即可检索或近似$ℝ{SHOIQ}$中任意概念的实例集。对比最先进推理器，结果表明EBR在缺失与错误数据下依然稳健。
原文摘要 · Abstract (English)
Concept learning exploits background knowledge in the form of description logic axioms to learn explainable classification models from knowledge bases. Despite recent breakthroughs in neuro-symbolic concept learning, most approaches still cannot be deployed on real-world knowledge bases. This is due to their use of description logic reasoners, which are not robust against inconsistencies nor erroneous data. We address this challenge by presenting a novel neural reasoner dubbed EBR. Our reasoner relies on embeddings to approximate the results of a symbolic reasoner. We show that EBR solely requires retrieving instances for atomic concepts and existential restrictions to retrieve or approximate the set of instances of any concept in the description logic $\mathcal{SHOIQ}$. In our experiments, we compare EBR with state-of-the-art reasoners. Our results suggest that EBR is robust against missing and erroneous data in contrast to existing reasoners.
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