We propose bounded fitting as a scheme for learning description logic concepts in the presence of ontologies. A main advantage is that the resulting learning algorithms come with theoretical guarantees regarding their generalization to unseen examples in the sense of PAC learning. We prove that, in contrast, several other natural learning algorithms fail to provide such guarantees. As a further contribution, we present the system SPELL which efficiently implements bounded fitting for the description logic $\mathcal{ELH}^r$ based on a SAT solver, and compare its performance to a state-of-the-art learner.

翻译：我们提出有界拟合作为一种在存在本体情况下学习描述逻辑概念的方案。其主要优势在于，所得到的学习算法在PAC学习意义上具有关于其泛化到未见示例的理论保证。相反，我们证明其他几种自然学习算法无法提供此类保证。作为进一步贡献，我们提出了SPELL系统，该系统基于SAT求解器高效实现了描述逻辑$\mathcal{ELH}^r$的有界拟合，并将其性能与最先进的学习器进行了比较。