A framework for linked data fusion and quality assessment

The growth of semantic web technologies underpins the ever-increasing development of linked data and their applications. In recent years, the number of linked data sources has been raised from 12 to more than 2973 sets. The datasets are managed as decentralized sources, and their quality is a serious concern. The assessment of the quality of linked data is a key to adopting them in different fields because each data set has been developed by a different group, using various methods and tools. Moreover, crowd sourcing contributes as one of the main strategies in data collection. This contribution is seen in the tourism industry or E-commerce fields and deserves attention. The qualitative and quantitative diversity of such data is higher than those generated by official organizations and firms. In this paper, we first overview and evaluate the dimensions and measures for the quality assessment of data. Then, we present a novel framework as a solution for improving linked data quality evaluation and data fusion. Finally, we adopt several tools to assess the quality of data of some reputable data sources using the proposed framework.