Machine Learning Meets the Semantic Web

Konstantinos Ilias Kotis (University of the Aegean)
Konstantina Zachila (University of the Aegean Dept. of Cultural Technology and Communication Intelligent Systems Lab Greece)
Evaggelos Paparidis (University of the Aegean Dept. of Cultural Technology and Communication Intelligent Systems Lab Greece)

Article ID: 3178

DOI: https://doi.org/10.30564/aia.v3i1.3178

Abstract


Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation.

Keywords


Knowledge graph;Semantic web;Ontology;Machine learning;Deep learning;Graph neural networks

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References


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Copyright © 2021 Konstantinos Ilias Kotis, Konstantina Zachila, Evaggelos Paparidis


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