A fuzzy loss for ontology classification
This addresses the need for more logically consistent models in ontology classification tasks, though it appears incremental as it builds on existing loss functions.
The authors tackled the problem of deep learning models lacking logical consistency in ontology classification by proposing a fuzzy loss that penalizes violations of subsumption and disjointness relations, resulting in a decrease in consistency violations by several orders of magnitude without harming classification performance.
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 fuzzy loss that combines label-based loss with terms penalising subsumption- or disjointness-violations. Our evaluation on the ChEBI ontology shows that the fuzzy 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 fuzzy loss for unsupervised learning. We show that this can further improve consistency on data from a