LD Sniffer: A Quality Assessment Tool for Measuring the Accessibility of Linked Data

Abstract

During the last decade, the Linked Open Data cloud has grown with much enthusiasm and a lot organizations are publishing their data as Linked Data. However, it is not evident whether enough efforts have been invested in maintaining those data or ensuring their quality. Data quality, defined as “fitness for use”, is an important aspect for Linked Data to be useful. Data consumers use quality indicators to decide whether or not to use a dataset in a given use case, which makes quality assessment of Linked Data an important activity. Accessibility, which is defined as the degree to which the data can be accessed, is a highly relevant quality characteristic to achieve the benefits of Linked Data. In this demo paper presents LD Sniffer, a web-based open source tool for performing quality assessment on the accessibility of Linked Data. It generates unambiguous and comparable assessment results with explicit semantics by defining both quality metrics as well as assessment results in RDF using the W3C Data Quality vocabulary. LD-Sniffer is also distributed as a Docker image improving ease of use with zero configurations.