1. ERBlox: Combining matching dependencies with machine learning for entity resolution
- Author
-
Zeinab Bahmani, Leopoldo Bertossi, and Nikolaos Vasiloglou
- Subjects
FOS: Computer and information sciences ,Computer Science - Artificial Intelligence ,Computer science ,02 engineering and technology ,Machine learning ,computer.software_genre ,Machine Learning (cs.LG) ,Theoretical Computer Science ,Datalog ,Computer Science - Databases ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,computer.programming_language ,Declarative programming ,Data processing ,business.industry ,Applied Mathematics ,Databases (cs.DB) ,16. Peace & justice ,Computer Science - Learning ,Artificial Intelligence (cs.AI) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Merge (version control) ,Software - 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., Final journal version, with some minor technical corrections. Extended version of arXiv:1508.06013
- Published
- 2017