Semantic Artificial Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Neural 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
Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publicationESWC 2020 Satellite Events
PublisherSpringer, Cham
Number of pages5
Publication statusAccepted/In press - 22 Apr 2020
EventExtended Semantic Web Conference 2020 - Crete, Greece
Duration: 31 May 20204 Jun 2020


ConferenceExtended Semantic Web Conference 2020
Abbreviated titleESWC2020
OtherVirtual Conference
Internet address

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  • Cite this

    Batsakis, S., Tachmazidis, I., Baryannis, G., & Antoniou, G. (Accepted/In press). Semantic Artificial Neural Networks. In The Semantic Web: ESWC 2020 Satellite Events Springer, Cham.