In this paper, we study a hybrid human-machine approach for solving the problem of Entity Resolution (ER). The goal of ER is to identify all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Our input is a graph over all the records in a database, where each edge has a probability denoting our prior belief (based on Machine Learning models) that the pair of records represented by the given edge are duplicates. Our objective is to resolve all the duplicates by asking humans to verify the equality of a subset of edges, leveraging the transitivity of the equality relation to infer the remaining edges (e.g. a = c can be inferred given a = b and b = c). We consider the problem of designing optimal strategies for asking questions to humans that minimize the expected number of questions asked. Using our theoretical framework, we analyze several strategies, and show that a strategy, claimed as "optimal" for this problem in a recent work, can perform arbitrarily bad in theory. We propose alternate strategies with theoretical guarantees. Using both public datasets as well as the production system at Facebook, we show that our techniques are e↵ective in practice.
REF applies a machine learned model that clusters similar records together (based on an associated probability of being a match).
Crowdsourcing Algorithms for Entity Resolution
PVLDB
PVLDB
Computer Science