Author ORCID Identifier

https://orcid.org/0000-0002-8135-3515

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

Proceedings of the 15th International Rule Challenge, 7th Industry Track, and 5th Doctoral Consortium @ RuleML+RR 2021 co-located with 17th Reasoning Web Summer School (RW 2021) and 13th DecisionCAMP 2021 as part of Declarative AI 2021

Abstract

The effective functioning of data-intensive applications usually requires that the dataset should be of high quality. The quality depends on the task they will be used for. However, it is possible to identify task-independent data quality dimensions which are solely related to data themselves and can be extracted with the help of rule mining/pattern mining. In order to assess and improve data quality, we propose an ontological approach to report data quality violated triples. Our goal is to provide data stakeholders with a set of methods and techniques to guide them in assessing and improving data quality

DOI

https://doi.org/10.21427/krpn-nh58

Funder

Science Foundation Ireland

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