  Many applications require explainable node classification in knowledge
graphs. Towards this end, a popular ``white-box'' approach is class expression
learning: Given sets of positive and negative nodes, class expressions in
description logics are learned that separate positive from negative nodes. Most
existing approaches are search-based approaches generating many candidate class
expressions and selecting the best one. However, they often take a long time to
find suitable class expressions. In this paper, we cast class expression
learning as a translation problem and propose a new family of class expression
learning approaches which we dub neural class expression synthesizers. Training
examples are ``translated'' into class expressions in a fashion akin to machine
translation. Consequently, our synthesizers are not subject to the runtime
limitations of search-based approaches. We study three instances of this novel
family of approaches based on LSTMs, GRUs, and set transformers, respectively.
An evaluation of our approach on four benchmark datasets suggests that it can
effectively synthesize high-quality class expressions with respect to the input
examples in approximately one second on average. Moreover, a comparison to
state-of-the-art approaches suggests that we achieve better F-measures on large
datasets. For reproducibility purposes, we provide our implementation as well
as pretrained models in our public GitHub repository at
https://github.com/dice-group/NeuralClassExpressionSynthesis
