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Article

Ontology-Based Feature Selection: A Survey

Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece
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Author to whom correspondence should be addressed.
Academic Editor: Davide Tosi
Future Internet 2021, 13(6), 158; https://doi.org/10.3390/fi13060158
Received: 5 May 2021 / Revised: 12 June 2021 / Accepted: 13 June 2021 / Published: 18 June 2021
(This article belongs to the Special Issue Software Engineering and Data Science)
The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic. View Full-Text
Keywords: feature selection; ontology; text classification; machine-learning feature selection; ontology; text classification; machine-learning
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MDPI and ACS Style

Sikelis, K.; Tsekouras, G.E.; Kotis, K. Ontology-Based Feature Selection: A Survey. Future Internet 2021, 13, 158. https://doi.org/10.3390/fi13060158

AMA Style

Sikelis K, Tsekouras GE, Kotis K. Ontology-Based Feature Selection: A Survey. Future Internet. 2021; 13(6):158. https://doi.org/10.3390/fi13060158

Chicago/Turabian Style

Sikelis, Konstantinos, George E. Tsekouras, and Konstantinos Kotis. 2021. "Ontology-Based Feature Selection: A Survey" Future Internet 13, no. 6: 158. https://doi.org/10.3390/fi13060158

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