Leveraging Matching Dependencies for Guided User Feedback in Linked Data Applications

This paper presents a new approach for managing integration quality and user feedback, for entity consolidation, within applications consuming Linked Open Data. The quality of a dataspace containing multiple linked datasets is defined in term of a utility measure, based on domain specific matching dependencies. Furthermore, the user is involved in the consolidation process through soliciting feedback about identity resolution links, where each candidate link is ranked according to its benefit to the dataspace; calculated by approximating the improvement in the utility of dataspace utility. The approach evaluated on real world and synthetic datasets demonstrates the effectiveness of utility measure; through dataspace integration quality improvement that requires less overall user feedback iterations.