  Approaches based on refinement operators have been successfully applied to
class expression learning on RDF knowledge graphs. These approaches often need
to explore a large number of concepts to find adequate hypotheses. This need
arguably stems from current approaches relying on myopic heuristic functions to
guide their search through an infinite concept space. In turn, deep
reinforcement learning provides effective means to address myopia by estimating
how much discounted cumulated future reward states promise. In this work, we
leverage deep reinforcement learning to accelerate the learning of concepts in
$\mathcal{ALC}$ by proposing DRILL -- a novel class expression learning
approach that uses a convolutional deep Q-learning model to steer its search.
By virtue of its architecture, DRILL is able to compute the expected discounted
cumulated future reward of more than $10^3$ class expressions in a second on
standard hardware. We evaluate DRILL on four benchmark datasets against
state-of-the-art approaches. Our results suggest that DRILL converges to goal
states at least 2.7$\times$ faster than state-of-the-art models on all
benchmark datasets. We provide an open-source implementation of our approach,
including training and evaluation scripts as well as pre-trained models.
