Data Quality Assessment in the Integration Process of Linked Open Data (LOD)

Abstract

Linked Open Data (LOD) entails a set of best practices for publishing and connecting structured data on the Web, which allows sharing and exchanging information in an inter-operable and reusable manner. The increasing adoption of these principles has lead to the creation of a globally distributed and huge informative space that covers various domains such as government, libraries, life sciences, and media. This offers a great opportunity to end-users to build semantic applications by exploring and consuming heterogeneous and dispersed possibly interlinked data. Thus, consuming linked data can be considered as a typical scenario of linked data integration in which a user requires to combine data residing in large and varying quality LOD datasets.In this paper, we examine the specifics of linked data integration and focus on three key challenges, namely data quality profiling and assessment, conflict resolution and quality improvement. We postulate that data quality assessment can act both as a deciding factor for conflict resolution and as an indicator of low quality data which need to be improved.