Entity resolution approaches for data quality

Abstract

Entity resolution is a key aspect of data quality, identifying which records correspond to the same real world entity in data sources. Entity resolution is a hot topic in both research communities and industries. We introduce three approaches to solve different aspects of entity resolution. The first approach learns entity resolution classifiers with genetic algorithm and active learning. The second approach proposes a solution for joint entity resolution. The third approach makes match decision for unsupervised entity resolution by graph clustering. All the three approaches are effective in entity resolution tasks.