Linked Data Quality Assessment through Network Analysis

Linked Data is at its core about the setting of links between resources. Links provide enriched semantics, pointers to extra information and enable the merging of data sets. However, as the amount of Linked Data has grown, there has been the need to automate the creation of links and such automated approaches can create low-quality links or unsuitable network structures. In particular, it is difficult to obtain an overall picture as to whether the links introduced improve or diminish the quality of Linked Data. In this work, we present an extensible framework that allows for the assessment of Linked Data quality from a global perspective. We test the framework on a set of known quality links and show that it effectively detects quality changes.