ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

TL;DR

ERBlox integrates machine learning classifiers, matching dependencies, and LogiQL to improve entity resolution by supporting blocking and merging processes with a declarative approach.

Contribution

This work introduces a novel framework combining ML, MDs, and LogiQL for enhanced entity resolution, demonstrating the benefits of their integration.

Findings

Improved accuracy in entity resolution tasks.

Effective support for blocking and merging phases.

Demonstrated advantages of declarative specifications.

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 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.