Semantic artificial neural networksSemantic artificial neural networks Σύντομη Δημοσίευση σε Συνέδριο Conference Short Paper 2022-06-032020enNeural networks have achieved in recent years human level performance in various application domains, including critical applications where accountability is a very important issue, closely related to the interpretability of neural networks and artificial intelligence in general. In this work, an approach for defining the structure of neural networks based on the conceptualisation and semantics of the application domain is proposed. The proposed approach, called Semantic Artificial Neural Networks, allows dealing with the problem of interpretability and also the definition of the structure of neural networks. In addition, the resulting neural networks are sparser and have fewer parameters than typical neural networks, while achieving high performance.http://creativecommons.org/licenses/by/4.0/39–44European Semantic Web Conference 2020 Satellite EventsThe Semantic Web: ESWC 2020 Satellite Events Batsakis Sotirios Μπατσακης Σωτηριος Tachmazidis Ilias Baryannis George Antoniou, Grigoris Springer Nature Neural networks Interpretability Semantic Web