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The role of community and social metrics in ontology evaluation: An interview study of ontology reuse

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conference contribution
posted on 2018-12-14, 11:25 authored by Marzieh Talebpour, Martin SykoraMartin Sykora, Tom JacksonTom Jackson
Finding a "good" or the "right" ontology for reuse is an ongoing challenge in the field of ontology engineering, where the main aim is to share and reuse existing semantics. This paper reports on a qualitative study with interviews of ontologists and knowledge engineers in different domains, ranging from biomedical field to manufacturing industry, and investigates the challenges they face while searching, evaluating, and selecting an ontology for reuse. Analysis of the interviews reveals diverse sets of quality metrics that are used when evaluating the quality of an ontology. While some of the metrics have already been mentioned in the literature, the findings from our study identify new sets of quality metrics such as community and social related metrics. We believe that this work represents a noteworthy contribution to the field of ontology engineering, with the hope that the research community can further draw on these initial findings in developing relevant quality metrics and ontology search and selection.

History

School

  • Business and Economics

Department

  • Business

Published in

IC3K 2017 - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Volume

2

Pages

119 - 127

Citation

TALEBPOUR, M., SYKORA, M.D. and JACKSON, T., 2017. The role of community and social metrics in ontology evaluation: An interview study of ontology reuse. IN: Aveiro, D., Dietz, J. and Filipe, J. (eds). Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017), Funchal, Madeira, Portugal, 1-3 November 2017, Volume 2: KEOD, pp.119-127.

Publisher

© SCITEPRESS – Science and Technology Publications

Version

  • 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/

Publication date

2017

Notes

This conference paper was published in Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Volume 2 and the definitive published version is available at https://doi.org/10.5220/0006589201190127.

ISBN

9789897582721

Language

  • en

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