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Quality Assessment Methodologies for Linked Open Data

The development and standardization of semantic web technologies have resulted in an unprecedented volume of data being published on the Web as Linked Open Data (LOD). However, we observe widely varying data quality ranging from extensively curated datasets to crowd-sourced and extracted data of relatively low quality. Data quality is commonly conceived as fitness of use. Consequently, a key challenge is to determine the data quality wrt. a particular use case. In this article, we present the results of a systematic review of approaches for assessing the data quality of LOD. We gather existing approaches and compare and group them under a common classification scheme. In particular, we unify and formalise commonly used terminologies across papers related to data quality. Additionally, a comprehensive list of the dimensions and metrics is presented. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and the development of new approaches focused towards data quality.