Entity resolution for probabilistic data

Entity resolution is the problem of identifying the tuples that represent the same real world entity. In this paper, we propose a complete solution to the problem of entity resolution over probabilistic data (ERPD), which arises in many applications that have to deal with probabilistic data. To deal with the ERPD problem, we distinguish between two classes of similarity functions, i.e. context-free and context-sensitive. We propose a PTIME algorithm for context-free similarity functions, and an approximation algorithm for context-sensitive similarity functions. We validated our algorithms through experiments over both synthetic and real datasets. Our extensive performance evaluation shows the effectiveness of our algorithms.