  Classifying nodes in knowledge graphs is an important task, e.g., for
predicting missing types of entities, predicting which molecules cause cancer,
or predicting which drugs are promising treatment candidates. While black-box
models often achieve high predictive performance, they are only post-hoc and
locally explainable and do not allow the learned model to be easily enriched
with domain knowledge. Towards this end, learning description logic concepts
from positive and negative examples has been proposed. However, learning such
concepts often takes a long time and state-of-the-art approaches provide
limited support for literal data values, although they are crucial for many
applications. In this paper, we propose EvoLearner - an evolutionary approach
to learn concepts in ALCQ(D), which is the attributive language with complement
(ALC) paired with qualified cardinality restrictions (Q) and data properties
(D). We contribute a novel initialization method for the initial population:
starting from positive examples, we perform biased random walks and translate
them to description logic concepts. Moreover, we improve support for data
properties by maximizing information gain when deciding where to split the
data. We show that our approach significantly outperforms the state of the art
on the benchmarking framework SML-Bench for structured machine learning. Our
ablation study confirms that this is due to our novel initialization method and
support for data properties.
