A Clustering-Based Framework for Incrementally Repairing Entity Resolution

Although entity resolution ( ER) is known to be an important problem that has wide-spread applications in many areas, including e-commerce, health-care, social science, and crime and fraud detection, one aspect that has largely been neglected is to monitor the quality of entity resolution and repair erroneous matching decisions over time. In this paper we develop an efficient method for incrementally repairing ER, i.e., fix detected erroneous matches and non-matches. Our method is based on an efficient clustering algorithm that eliminates inconsistencies among matching decisions, and an efficient provenance indexing data structure that allows us to trace the evidence of clustering for supporting ER repairing. We have evaluated our method over real-world databases, and our experimental results show that the quality of entity resolution can be significantly improved through repairing over time.