  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.
