# Progressive Entity Resolution: A Design Space Exploration

arXiv: https://arxiv.org/abs/2503.08298 · HTML: https://mlsanity.com/paper/2503.08298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [Labeling Training Data for Entity Matching Using Large Language Models](https://mlsanity.com/paper/2606.28823/) (2026)

## Abstract (arXiv)

Entity Resolution (ER) is typically implemented as a batch task that processes all available data before identifying duplicate records. However, applications with time or computational constraints, e.g., those running in the cloud, require a progressive approach that produces results in a pay-as-you-go fashion. Numerous algorithms have been proposed for Progressive ER in the literature. In this work, we propose a novel framework for Progressive Entity Resolution that organizes relevant techniques into four consecutive steps: (i) filtering, which reduces the search space to the most likely candidate matches, (ii) weighting, which associates every pair of candidate matches with a similarity score, (iii) scheduling, which prioritizes the execution of the candidate matches so that the real duplicates precede the non-matching pairs, and (iv) matching, which applies a complex, matching function to the pairs in the order defined by the previous step. We associate each step with existing and novel techniques, illustrating that our framework overall generates a superset of the main existing works in the field. We select the most representative combinations resulting from our framework and fine-tune them over 10 established datasets for Record Linkage and 8 for Deduplication, with our results indicating that our taxonomy yields a wide range of high performing progressive techniques both in terms of effectiveness and time efficiency.