Journal
DATA & KNOWLEDGE ENGINEERING
Volume 133, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.datak.2021.101889
Keywords
Ontology; Ontology fixing; Ontology quality measures; Ontology improvement methodology; Deming cycle
Funding
- Xunta de Galicia [ED481B 2017/018]
- Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) , Spain [ED431C2018/55-GRC]
- Spanish Ministry of Economy, Industry and Competitiveness (SMEIC) [TIN2017-84658-C2-1-R]
- State Research Agency (SRA)
- European Regional Development Fund (ERDF)
Ask authors/readers for more resources
This paper introduces a methodology for evaluating and fixing quality issues of ontologies generated from natural language texts, aiming to minimize design defects. The proposed methodology, based on the Deming cycle and leveraging effective quality standards from the software engineering domain, has the potential to be extended to knowledge engineering quality management.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available