Evaluating the Quality of the LOD Cloud: An Empirical Investigation

Tracking #: 1491-2703

This paper is currently under review
Authors: 
Jeremy Debattista
Christoph Lange
Sören Auer

Responsible editor: 
Ruben Verborgh

Submission type: 
Survey Article
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 reflects. The diversity of the Web of Data is reflected in the quality of the Web of Data.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 (~ 5 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 published the quality metadata for each assessed dataset as Linked Data, using the Dataset Quality Vocabulary. This metadata could then be 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. In other words, the PCA will help us to identify the non-informative metrics.
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