Data Quality Assessment of Comma Separated Values Using Linked Data Approach

Aparna Nayak; Bojan Božić; Luca Longo

doi:10.1007/978-3-031-04216-4_22

Data Quality Assessment of Comma Separated Values Using Linked Data Approach

Aparna Nayak, Bojan Božić, Luca Longo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

With an increasing amount of structured data on the web, the need to understand and convert it into linked data is growing. One of the most frequent data formats is Comma Separated Value (CSV). However, it is not easy to describe metadata such as the datatype, data quality and data provenance along with it. Therefore, to publish CSV on the web, it is required to convert CSV into linked data format. Many approaches exist to facilitate the conversion process from structured data to linked data. However, all methods require additional domain knowledge for the conversion process. The goal of this research is to assist publishers in converting CSV files into linked data without human intervention whilst understanding its quality and root causes of data quality violations. The proposed framework consists of two modules. The first module converts the given CSV file into a knowledge graph based on a proposed ontology which is appended with data quality information. In the second module, triples that have violated the data quality constraints are identified. The results show that it is possible to convert a CSV to a knowledge graph by adding its quality information without the help of external mappings.

Original language	English
Title of host publication	Business Information Systems Workshops - BIS 2021 International Workshops, Revised Selected Papers
Editors	Witold Abramowicz, Sören Auer, Milena Stróżyna
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	240-250
Number of pages	11
ISBN (Print)	9783031042157
DOIs	https://doi.org/10.1007/978-3-031-04216-4_22
Publication status	Published - 2022
Event	24th International Conference on Business Information Systems, BIS 2021 - Virtual, Online Duration: 14 Jun 2021 → 17 Jun 2021

Publication series

Name	Lecture Notes in Business Information Processing
Volume	444 LNBIP
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	24th International Conference on Business Information Systems, BIS 2021
City	Virtual, Online
Period	14/06/21 → 17/06/21

Keywords

CSV
Data quality
Knowledge graphs
Linked data
Quality assessment
Root cause analysis

Access to Document

10.1007/978-3-031-04216-4_22

Cite this

Nayak, A., Božić, B., & Longo, L. (2022). Data Quality Assessment of Comma Separated Values Using Linked Data Approach. In W. Abramowicz, S. Auer, & M. Stróżyna (Eds.), Business Information Systems Workshops - BIS 2021 International Workshops, Revised Selected Papers (pp. 240-250). (Lecture Notes in Business Information Processing; Vol. 444 LNBIP). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-04216-4_22

Nayak, Aparna ; Božić, Bojan ; Longo, Luca. / Data Quality Assessment of Comma Separated Values Using Linked Data Approach. Business Information Systems Workshops - BIS 2021 International Workshops, Revised Selected Papers. editor / Witold Abramowicz ; Sören Auer ; Milena Stróżyna. Springer Science and Business Media Deutschland GmbH, 2022. pp. 240-250 (Lecture Notes in Business Information Processing).

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title = "Data Quality Assessment of Comma Separated Values Using Linked Data Approach",

abstract = "With an increasing amount of structured data on the web, the need to understand and convert it into linked data is growing. One of the most frequent data formats is Comma Separated Value (CSV). However, it is not easy to describe metadata such as the datatype, data quality and data provenance along with it. Therefore, to publish CSV on the web, it is required to convert CSV into linked data format. Many approaches exist to facilitate the conversion process from structured data to linked data. However, all methods require additional domain knowledge for the conversion process. The goal of this research is to assist publishers in converting CSV files into linked data without human intervention whilst understanding its quality and root causes of data quality violations. The proposed framework consists of two modules. The first module converts the given CSV file into a knowledge graph based on a proposed ontology which is appended with data quality information. In the second module, triples that have violated the data quality constraints are identified. The results show that it is possible to convert a CSV to a knowledge graph by adding its quality information without the help of external mappings.",

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Nayak, A , Božić, B & Longo, L 2022, Data Quality Assessment of Comma Separated Values Using Linked Data Approach. in W Abramowicz, S Auer & M Stróżyna (eds), Business Information Systems Workshops - BIS 2021 International Workshops, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 444 LNBIP, Springer Science and Business Media Deutschland GmbH, pp. 240-250, 24th International Conference on Business Information Systems, BIS 2021, Virtual, Online, 14/06/21. https://doi.org/10.1007/978-3-031-04216-4_22

Data Quality Assessment of Comma Separated Values Using Linked Data Approach. / Nayak, Aparna ; Božić, Bojan ; Longo, Luca.
Business Information Systems Workshops - BIS 2021 International Workshops, Revised Selected Papers. ed. / Witold Abramowicz; Sören Auer; Milena Stróżyna. Springer Science and Business Media Deutschland GmbH, 2022. p. 240-250 (Lecture Notes in Business Information Processing; Vol. 444 LNBIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Data Quality Assessment of Comma Separated Values Using Linked Data Approach

AU - Nayak, Aparna

AU - Božić, Bojan

AU - Longo, Luca

PY - 2022

Y1 - 2022

N2 - With an increasing amount of structured data on the web, the need to understand and convert it into linked data is growing. One of the most frequent data formats is Comma Separated Value (CSV). However, it is not easy to describe metadata such as the datatype, data quality and data provenance along with it. Therefore, to publish CSV on the web, it is required to convert CSV into linked data format. Many approaches exist to facilitate the conversion process from structured data to linked data. However, all methods require additional domain knowledge for the conversion process. The goal of this research is to assist publishers in converting CSV files into linked data without human intervention whilst understanding its quality and root causes of data quality violations. The proposed framework consists of two modules. The first module converts the given CSV file into a knowledge graph based on a proposed ontology which is appended with data quality information. In the second module, triples that have violated the data quality constraints are identified. The results show that it is possible to convert a CSV to a knowledge graph by adding its quality information without the help of external mappings.

AB - With an increasing amount of structured data on the web, the need to understand and convert it into linked data is growing. One of the most frequent data formats is Comma Separated Value (CSV). However, it is not easy to describe metadata such as the datatype, data quality and data provenance along with it. Therefore, to publish CSV on the web, it is required to convert CSV into linked data format. Many approaches exist to facilitate the conversion process from structured data to linked data. However, all methods require additional domain knowledge for the conversion process. The goal of this research is to assist publishers in converting CSV files into linked data without human intervention whilst understanding its quality and root causes of data quality violations. The proposed framework consists of two modules. The first module converts the given CSV file into a knowledge graph based on a proposed ontology which is appended with data quality information. In the second module, triples that have violated the data quality constraints are identified. The results show that it is possible to convert a CSV to a knowledge graph by adding its quality information without the help of external mappings.

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KW - Knowledge graphs

KW - Linked data

KW - Quality assessment

KW - Root cause analysis

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DO - 10.1007/978-3-031-04216-4_22

M3 - Conference contribution

AN - SCOPUS:85128719049

SN - 9783031042157

T3 - Lecture Notes in Business Information Processing

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BT - Business Information Systems Workshops - BIS 2021 International Workshops, Revised Selected Papers

A2 - Abramowicz, Witold

A2 - Auer, Sören

A2 - Stróżyna, Milena

PB - Springer Science and Business Media Deutschland GmbH

T2 - 24th International Conference on Business Information Systems, BIS 2021

Y2 - 14 June 2021 through 17 June 2021

ER -

Nayak A , Božić B , Longo L. Data Quality Assessment of Comma Separated Values Using Linked Data Approach. In Abramowicz W, Auer S, Stróżyna M, editors, Business Information Systems Workshops - BIS 2021 International Workshops, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 240-250. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-031-04216-4_22

Data Quality Assessment of Comma Separated Values Using Linked Data Approach

Abstract

Publication series

Conference

Keywords

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Cite this