Affiliations: [a] ADAPT Centre, School of Computer Science and Statistics, Trinity College Dublin, Ireland. E-mail: [email protected] | [b] Fraunhofer IAIS, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. E-mail: [email protected] | [c] TIB Leibniz Information Centre for Science and Technology & Leibniz University of Hannover, Am Welfengarten 1b, 30167 Hannover, Germany. E-mail: [email protected] | [d] Faculty of Economics, Management and Accountancy, University of Malta, Msida MSD 2080, Malta. E-mail: [email protected]
Abstract: The increasing adoption of the Linked Data principles brought with it an unprecedented dimension to the Web, transforming the traditional Web of Documents to a vibrant information ecosystem, also known as the Web of Data. This transformation, however, does not come without any pain points. Similar to the Web of Documents, the Web of Data is heterogenous in terms of the various domains it covers. The diversity of the Web of Data is also reflected in its quality. Data quality impacts the fitness for use of the data for the application at hand, and choosing the right dataset is often a challenge for data consumers. In this quantitative empirical survey, we analyse 130 datasets (≈ 3.7 billion quads), extracted from the latest Linked Open Data Cloud using 27 Linked Data quality metrics, and provide insights into the current quality conformance. Furthermore, we publish the quality metadata for each assessed dataset as Linked Data, using the Dataset Quality Vocabulary (daQ). This metadata is then used by data consumers to search and filter possible datasets based on different quality criteria. Thereafter, based on our empirical study, we present an aggregated view of the Linked Data quality in general. Finally, using the results obtained from the quality assessment empirical study, we use the Principal Component Analysis (PCA) test in order to identify the key quality indicators that can give us sufficient information about a dataset’s quality.
Keywords: Data quality, Linked Data, empirical study, data quality survey
DOI: 10.3233/SW-180306
Journal: Semantic Web, vol. 9, no. 6, pp. 859-901, 2018
