用可满足性求解器实现高阶逻辑概念学习，兼顾理论保障与实际效率。
Bounded Fitting for Expressive Description Logics
- 基于SAT求解器的有界拟合方法，支持带逆关系等复杂逻辑结构
- 在扩展ALC的表达性逻辑中仍保持近似正确学习的理论保证
- 实测性能优于当前主流概念学习工具，适合复杂知识建模场景
有界拟合是一种从标注数据中学习逻辑公式的有效范式，提供类似PAC的泛化保证，并可借助SAT求解器实现。该方法已成功应用于ALC描述逻辑中的概念学习。本文研究其在扩展ALC的表达性描述逻辑中的应用，包括逆关系、限定数量限制和特征比较。我们分析了在何种条件下有界拟合仍保持良好理论性质，并使用SAT求解器实现了该方法。实验表明，该工具在性能上优于当前最先进的概念学习器，验证了其在高阶逻辑概念学习中的实用性。
原文摘要 · Abstract (English)
Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and can often be implemented leveraging SAT solvers. It has been successfully applied to learning concepts of the description logic ALC. We study bounded fitting for learning concepts in expressive description logics that extend ALC with inverse roles, qualified number restrictions, and feature comparisons. We investigate under which conditions bounded fitting keeps its favorable theoretical properties in this setting, and implement it using a SAT solver. We compare our tool with state-of-the-art concept learners with encouraging results, demonstrating that it is a practical approach to expressive concept learning.
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