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Ontology selection for reuse: Will it ever get easier?
conference contribution
posted on 2018-11-19, 14:32 authored by Marzieh Talebpour, Martin SykoraMartin Sykora, Tom JacksonTom JacksonOntologists and knowledge engineers tend to examine different aspects of ontologies when assessing their suitability for reuse. However, most of the evaluation metrics and frameworks introduced in the literature
are based on a limited set of internal characteristics of ontologies and dismiss how the community uses and evaluates them. This paper used a survey questionnaire to explore, clarify and also confirm the importance of the set of quality related metrics previously found in the literature and an interview study. According to the 157 responses collected from ontologists and knowledge engineers, the process of ontology selection for reuse depends on different social and community related metrics and metadata. We believe that the findings
of this research can contribute to facilitating the process of selecting an ontology for reuse.
History
School
- Business and Economics
Department
- Business
Published in
10th International Conference on Knowledge Engineering and Ontology Development - KEOD 2018Citation
TALEBPOUR, M., SYKORA, M.D. and JACKSON, T., 2018. Ontology selection for reuse: Will it ever get easier? IN: Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KEOD 2018), Seville, Spain, 10-20th Sept. Setubal, Portugal: Scitepress, Vol 2, pp. 108-116.Publisher
Science and Technology PublicationsVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2018-08-01Publication date
2018Notes
This is a conference paper. KEOD is part of IC3K, the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management.ISBN
9789897583308Publisher version
Language
- en