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Ontology-Based Feature Selection: A Survey

Author

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  • Konstantinos Sikelis

    (Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece)

  • George E. Tsekouras

    (Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece)

  • Konstantinos Kotis

    (Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece)

Abstract

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.

Suggested Citation

  • Konstantinos Sikelis & George E. Tsekouras & Konstantinos Kotis, 2021. "Ontology-Based Feature Selection: A Survey," Future Internet, MDPI, vol. 13(6), pages 1-28, June.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:6:p:158-:d:577117
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    References listed on IDEAS

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    1. Kühl, N. & Mühlthaler, M. & Goutier, Marc, 2020. "Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130106, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Alzamil, Zamil & Appelbaum, Deniz & Nehmer, Robert, 2020. "An ontological artifact for classifying social media: Text mining analysis for financial data," International Journal of Accounting Information Systems, Elsevier, vol. 38(C).
    3. Lina Zhou & Pimwadee Chaovalit, 2008. "Ontology‐supported polarity mining," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(1), pages 98-110, January.
    4. Niklas Kühl & Marius Mühlthaler & Marc Goutier, 2020. "Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 351-367, June.
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    Cited by:

    1. Davide Tosi, 2022. "Editorial for the Special Issue on “Software Engineering and Data Science”," Future Internet, MDPI, vol. 14(11), pages 1-2, October.

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