  Deep learning models are often unaware of the inherent constraints of the
task they are applied to. However, many downstream tasks require logical
consistency. For ontology classification tasks, such constraints include
subsumption and disjointness relations between classes.
  In order to increase the consistency of deep learning models, we propose a
semantic loss that combines label-based loss with terms penalising subsumption-
or disjointness-violations. Our evaluation on the ChEBI ontology shows that the
semantic loss is able to decrease the number of consistency violations by
several orders of magnitude without decreasing the classification performance.
In addition, we use the semantic loss for unsupervised learning. We show that
this can further improve consistency on data from a distribution outside the
scope of the supervised training.
