A Linked Data Profiling Service for Quality Assessment

Nandana Mihindukulasooriya, Raúl García-Castro, Freddy Priyatna, Edna Ruckhaus, Nelson Saturno

Abstract: The Linked (Open) Data cloud has been growing at a rapid rate in recent years. However, the large variance of quality in its datasets is a key obstacle that hinders their use, so quality assessment has become an important aspect. Data profiling is one of the widely used techniques for data quality assessment in domains such as relational data; nevertheless, it is not so widely used in Linked Data. We argue that one reason for this is the lack of Linked Data profiling tools that are configurable in a declarative manner, and that produce comprehensive profiling information with the level of detail required by quality assessment techniques. To this end, this demo paper presents the Loupe API, a RESTful web service that profiles Linked Data based on user requirements and produces comprehensive profiling information on explicit RDF general data, class, property and vocabulary usage, and implicit data patterns such as cardinalities, instance ratios, value distributions, and multilingualism. Profiling results can be used to assess quality either by manual inspection, or automatically using data validation languages such as SHACL, ShEX, or SPIN.