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 three components of ER: (a) Classifiers for duplicate/non-duplicate record pairs built using machine learning (ML) techniques, (b) MDs for supporting both the blocking phase of ML and the merge itself; and (c) The use of the declarative language LogiQL -an extended form of Datalog supported by the LogicBlox platform- for 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/10.1007/978-3-319-23540-0_27
Citation
Bahmani, Z. (Zeinab), Bertossi, L, & Vasiloglou, N. (Nikolaos). (2015). ERBlox: Combining matching dependencies with machine learning for entity resolution. doi:10.1007/978-3-319-23540-0_27