(Linked) data quality assessment: An ontological approach

Aparna Nayak; Bojan Božić; Luca Longo

doi:10.21427/krpn-nh58

(Linked) data quality assessment: An ontological approach

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Number of pages	8
Journal	CEUR Workshop Proceedings
Volume	2956
DOIs	https://doi.org/10.21427/krpn-nh58
Publication status	Published - 2021
Event	15th International Rule Challenge, 7th Industry Track, and 5th Doctoral Consortium @ RuleML+RR 2021, RuleML+RR-Companion 2021 - Virtual, Leuven, Belgium Duration: 8 Sep 2021 → 15 Sep 2021

Keywords

Data quality assessment
Data quality improvement
Linked data
Root cause analysis

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10.21427/krpn-nh58Licence: CC BY-NC-SA

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title = "(Linked) data quality assessment: An ontological approach",

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.",

keywords = "Data quality assessment, Data quality improvement, Linked data, Root cause analysis",

author = "Aparna Nayak and Bojan Bo{\v z}i{\'c} and Luca Longo",

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T2 - 15th International Rule Challenge, 7th Industry Track, and 5th Doctoral Consortium @ RuleML+RR 2021, RuleML+RR-Companion 2021

AU - Nayak, Aparna

AU - Božić, Bojan

AU - Longo, Luca

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Data quality assessment

KW - Data quality improvement

KW - Linked data

KW - Root cause analysis

UR - https://www.scopus.com/pages/publications/85116276323

U2 - 10.21427/krpn-nh58

DO - 10.21427/krpn-nh58

M3 - Conference article

AN - SCOPUS:85116276323

SN - 1613-0073

VL - 2956

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 8 September 2021 through 15 September 2021

ER -

(Linked) data quality assessment: An ontological approach

Abstract

Keywords

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