Improving Linked Data Quality Assessment and Fusion by a Conflict Resolution Approach

Editorial

Authors

1 Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

2 Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

3 School of Computer Science, Carleton University, Ottawa, Canada

Abstract

The semantic web technology and decision making based on the linked data is progressing every day. The linked data 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. 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 especially linked data. Then, we present a novel framework as a solution for improving linked data quality assessment and data fusion. The good quality data make good result in data fusion. Finally, we introduce six rules for handling data conflicts and a new metric for assessment of granularity level of data (GLQM) and adopt several tools to assess the quality of data using the proposed framework.

Keywords

References

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