A neural network (NN) is known to be an efficient and learnable tool supporting decision-making processes particularly in Industry 4.0. The majority of NNs are data-driven and, therefore, depend on training data quantity and quality. The current trend in enhancing data-driven models with knowledge-based models promises to enable effective NNs with less data. So-called physics-informed NNs use additional knowledge from computational science to improve NN training. Quite much of the knowledge is available as logical constraints from domain ontologies, and NNs may benefit from using it. In this paper, we study the concept of Taxonomy-Informed NN (TINN), which combines data-driven training of NNs with ontological knowledge. We study different patterns of NN training with additional knowledge on class-subclass hierarchies and instance-class relationships with potential for federated learning. Our experiments show that additional knowledge, which influences TINNs’ training process through the loss function at backpropagation, improves the quality of trained models. See presentation slides: https://ai.it.jyu.fi/ISM-2023-TINN.pptx