Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4945337 | International Journal of Approximate Reasoning | 2017 | 24 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zeinab Bahmani, Leopoldo Bertossi, Nikolaos Vasiloglou,