Affiliations: [a] Universität Leipzig, Institut für Informatik, D-04103 Leipzig, Germany. E-mails: [email protected], [email protected] | [b] University of Milano-Bicocca, Department of Computer Science, Systems and Communication (DISCo), Innovative Techonologies for Interaction and Services (Lab), Viale Sarca 336, Milan, Italy. E-mails: [email protected], [email protected] | [c] Associate Professor and Vice Chair of Surgery, Duke University, Durham, NC, USA. E-mail: [email protected] | [d] University of Bonn, Computer Science Department, Enterprise Information Systems and Fraunhofer IAIS, Germany. E-mail: [email protected]
Note: [**] These authors contributed equally to this work.
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.
Keywords: Data quality, Linked Data, assessment, survey
DOI: 10.3233/SW-150175
Journal: Semantic Web, vol. 7, no. 1, pp. 63-93, 2016
