Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language LogiQL—an extended form of Datalog supported by the LogicBlox platform—for all activities related to data processing, and the specification and enforcement of MDs.

Additional Metadata
Keywords Classification, Datalog, Entity resolution, Matching dependencies, Support-vector machines
Persistent URL dx.doi.org/10.1016/j.ijar.2017.01.003
Journal International Journal of Approximate Reasoning
Bahmani, Z. (Zeinab), Bertossi, L, & Vasiloglou, N. (Nikolaos). (2017). ERBlox: Combining matching dependencies with machine learning for entity resolution. International Journal of Approximate Reasoning, 83, 118–141. doi:10.1016/j.ijar.2017.01.003