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Semi-automatic construction of a domain ontology for wind energy using Wikipedia articles

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  • Küçük, Dilek
  • Arslan, Yusuf

Abstract

Domain ontologies are important information sources for knowledge-based systems. Yet, building domain ontologies from scratch is known to be a very labor-intensive process. In this study, we present our semi-automatic approach to building an ontology for the domain of wind energy which is an important type of renewable energy with a growing share in electricity generation all over the world. Related Wikipedia articles are first processed in an automated manner to determine the basic concepts of the domain together with their properties and next the concepts, properties, and relationships are organized to arrive at the ultimate ontology. We also provide pointers to other engineering ontologies which could be utilized together with the proposed wind energy ontology in addition to its prospective application areas. The current study is significant as, to the best of our knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process.

Suggested Citation

  • Küçük, Dilek & Arslan, Yusuf, 2014. "Semi-automatic construction of a domain ontology for wind energy using Wikipedia articles," Renewable Energy, Elsevier, vol. 62(C), pages 484-489.
    • Handle: RePEc:eee:renene:v:62:y:2014:i:c:p:484-489
    DOI: 10.1016/j.renene.2013.08.002
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    References listed on IDEAS

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    1. Pipattanasomporn, Manisa & Feroze, Hassan & Rahman, Saifur, 2012. "Securing critical loads in a PV-based microgrid with a multi-agent system," Renewable Energy, Elsevier, vol. 39(1), pages 166-174.
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    Cited by:

    1. Andrea Michiorri & Anna Maria Sempreviva & Sean Philipp & Paula Perez-Lopez & Alain Ferriere & David Moser, 2022. "Topic Taxonomy and Metadata to Support Renewable Energy Digitalisation," Energies, MDPI, vol. 15(24), pages 1-23, December.
    2. Alejandro Blanco-M. & Pere Marti-Puig & Karina Gibert & Jordi Cusidó & Jordi Solé-Casals, 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History," Energies, MDPI, vol. 12(10), pages 1-20, May.

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