# ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

arXiv: https://arxiv.org/abs/1602.02334 · HTML: https://mlsanity.com/paper/1602.02334/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2016.

## Built on by (most cited first, 2 of 2)

- [Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria](https://mlsanity.com/paper/2508.10504/) (2025)
- [Transformer-Gather, Fuzzy-Reconsider: A Scalable Hybrid Framework for Entity Resolution](https://mlsanity.com/paper/2509.17470/) (2025)

## Abstract (arXiv)

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.