The development and standardization of Semantic Web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.

Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S. (2016). Quality assessment for Linked Data: A Survey. SEMANTIC WEB, 7(1), 63-93 [10.3233/SW-150175].

Quality assessment for Linked Data: A Survey

Rula, A;Maurino, A;
2016

Abstract

The development and standardization of Semantic Web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.
Articolo in rivista - Articolo scientifico
assessment; Data quality; Linked Data; survey; Computer Networks and Communications; Computer Science Applications; Information Systems
English
2016
2016
7
1
63
93
partially_open
Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S. (2016). Quality assessment for Linked Data: A Survey. SEMANTIC WEB, 7(1), 63-93 [10.3233/SW-150175].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/96841
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