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Semantic artificial neural networks

Batsakis Sotirios, Tachmazidis Ilias, Baryannis George, Antoniou, Grigoris

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URIhttp://purl.tuc.gr/dl/dias/8535DAA2-B92D-434B-AFCF-5DFD48D00DFC-
Identifierhttps://doi.org/10.1007/978-3-030-62327-2_7-
Identifierhttps://link.springer.com/chapter/10.1007/978-3-030-62327-2_7-
Languageen-
Extent6 pagesen
TitleSemantic artificial neural networksen
CreatorBatsakis Sotiriosen
CreatorΜπατσακης Σωτηριοςel
CreatorTachmazidis Iliasen
CreatorBaryannis Georgeen
CreatorAntoniou, Grigorisen
PublisherSpringer Natureen
Content SummaryNeural 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.en
Type of ItemΣύντομη Δημοσίευση σε Συνέδριοel
Type of ItemConference Short Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2022-06-03-
Date of Publication2020-
SubjectNeural networksen
SubjectInterpretabilityen
SubjectSemantic Weben
Bibliographic CitationS. Batsakis, I. Tachmazidis, G. Baryannis, and G. Antoniou, “Semantic artificial neural networks,” in The Semantic Web: ESWC 2020 Satellite Events, vol 12124, Lecture Notes in Computer Science, A. Harth, V. Presutti, R. Troncy, M. Acosta, A. Polleres, J. D. Fernández, J. Xavier Parreira, O. Hartig, K. Hose, M. Cochez, Eds., Cham, Switzerland: Springer Nature, 2020, pp. 39–44, doi: 10.1007/978-3-030-62327-2_7.en

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