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A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

Author

Listed:
  • Marwa Abdelreheim

    (Department of Information Systems, Faculty of Computers and Information, Assiut University, Assiut 71515, Egypt)

  • Taysir Hassan A. Soliman

    (Department of Information Systems, Faculty of Computers and Information, Assiut University, Assiut 71515, Egypt)

  • Friederike Klan

    (German Aerospace Center, Institute of Data Science, Maelzerstraße 3-5, 07743 Jena, Germany)

Abstract

The profusion of existing ontologies in different domains has made reusing ontologies a best practice when developing new ontologies. The ontology reuse process reduces the expensive cost of developing a new ontology, in terms of time and effort, and supports semantic interoperability. Existing ontology development tools do not assist in the recommendation of ontologies or their concepts to be reused. Also, existing ontology recommendation tools could suggest whole ontologies covering a set of input keywords without referring to which parts of them (e.g., concepts) can be reused. In this paper, we propose an effective ontology recommendation system that helps the user in the iterative development and reuse of ontologies. The system allows the user to provide explicit preferences about the new ontology, and iteratively guides the user to parts from existing ontologies which match his preferences for reuse. Finally, we developed a prototype of our ontology recommendation system and conducted a user-based evaluation to assess the effectiveness of our approach.

Suggested Citation

  • Marwa Abdelreheim & Taysir Hassan A. Soliman & Friederike Klan, 2023. "A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse," Future Internet, MDPI, vol. 15(10), pages 1-21, October.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:331-:d:1255024
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    Cited by:

    1. Salvatore Calcagno & Andrea Calvagna & Emiliano Tramontana & Gabriella Verga, 2024. "Merging Ontologies and Data from Electronic Health Records," Future Internet, MDPI, vol. 16(2), pages 1-16, February.

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