# Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples

arXiv: https://arxiv.org/abs/2103.04489 · HTML: https://mlsanity.com/paper/2103.04489/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2021.

## Built on by (most cited first, 15 of 15)

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- [Learning High-Quality and General-Purpose Phrase Representations](https://mlsanity.com/paper/2401.10407/) (2024)
- [Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns](https://mlsanity.com/paper/2502.16813/) (2025)
- [TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations](https://mlsanity.com/paper/2411.17110/) (2024)
- [MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching](https://mlsanity.com/paper/2308.01927/) (2023)
- [Unlocking the Power of Large Language Models for Multi-table Entity Matching](https://mlsanity.com/paper/2604.21238/) (2026)
- [ShallowBlocker: Improving Set Similarity Joins for Blocking](https://mlsanity.com/paper/2312.15835/) (2023)
- [A Set-to-Set Distance Measure in Hyperbolic Space](https://mlsanity.com/paper/2506.18529/) (2025)
- [Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis](https://mlsanity.com/paper/2606.03812/) (2026)
- [MosaicJoin: Compact Semantic Sketches for Value-Level Join Discovery](https://mlsanity.com/paper/2607.21781/) (2026)

## Abstract (arXiv)

Fuzzy similarity join is an important database operator widely used in practice. So far the research community has focused exclusively on optimizing fuzzy join textitscalability. However, practitioners today also struggle to optimize fuzzy-join textitquality, because they face a daunting space of parameters (e.g., distance-functions, distance-thresholds, tokenization-options, etc.), and often have to resort to a manual trial-and-error approach to program these parameters in order to optimize fuzzy-join quality. This key challenge of automatically generating high-quality fuzzy-join programs has received surprisingly little attention thus far. In this work, we study the problem of "auto-program" fuzzy-joins. Leveraging a geometric interpretation of distance-functions, we develop an unsupervised textscAuto-FuzzyJoin framework that can infer suitable fuzzy-join programs on given input tables, without requiring explicit human input such as labeled training data. Using textscAuto-FuzzyJoin, users only need to provide two input tables L and R, and a desired precision target τ (say 0.9). textscAuto-FuzzyJoin leverages the fact that one of the input is a reference table to automatically program fuzzy-joins that meet the precision target τ in expectation, while maximizing fuzzy-join recall (defined as the number of correctly joined records). Experiments on both existing benchmarks and a new benchmark with 50 fuzzy-join tasks created from Wikipedia data suggest that the proposed textscAuto-FuzzyJoin significantly outperforms existing unsupervised approaches, and is surprisingly competitive even against supervised approaches (e.g., Magellan and DeepMatcher) when 50% of ground-truth labels are used as training data.