EvoLearner: Learning Description Logics with Evolutionary Algorithms

Stefan Heindorf, Lukas Blübaum, Nick Düsterhus, Till Werner, Varun Nandkumar Golani, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Abstract:

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-theart 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.