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An ontology knowledge inspection methodology for quality assessment and continuous improvement

Authors :
José Ramon Méndez
Gabriela R. Roldán-Molina
Vitor Basto-Fernandes
David Ruano-Ordás
Source :
Investigo. Repositorio Institucional de la Universidade de Vigo, Universidade de Vigo (UVigo)
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

Ontology-learning methods were introduced in the knowledge engineering area to automatically build ontologies from natural language texts related to a domain. Despite the initial appeal of these methods, automatically generated ontologies may have errors, inconsistencies, and a poor design quality, all of which must be manually fixed, in order to maintain the validity and usefulness of automated output. In this work, we propose a methodology to assess ontologies quality (quantitatively and graphically) and to fix ontology inconsistencies minimizing design defects. The proposed methodology is based on the Deming cycle and is grounded on quality standards that proved effective in the software engineering domain and present high potential to be extended to knowledge engineering quality management. This paper demonstrates that software engineering quality assessment approaches and techniques can be successfully extended and applied to the ontology-fixing and quality improvement problem. The proposed methodology was validated in a testing ontology, by ontology design quality comparison between a manually created and automatically generated ontology. Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG Xunta de Galicia | Ref. ED481B 2017/018 Xunta de Galicia | Ref. ED431C2018 / 55-GRC Ministerio de Economía, Industria y Competitividad | Ref. TIN2017-84658-C2-1-R

Details

ISSN :
0169023X
Volume :
133
Database :
OpenAIRE
Journal :
Data & Knowledge Engineering
Accession number :
edsair.doi.dedup.....fd89cc0c577fcffcc842047c561518db
Full Text :
https://doi.org/10.1016/j.datak.2021.101889