# Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation

arXiv: https://arxiv.org/abs/2207.04122 · HTML: https://mlsanity.com/paper/2207.04122/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2022.

## Built on by (most cited first, 19 of 19)

- [Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.16884/) (2024)
- [Entity Matching using Large Language Models](https://mlsanity.com/paper/2310.11244/) (2023)
- [WDC Products: A Multi-Dimensional Entity Matching Benchmark](https://mlsanity.com/paper/2301.09521/) (2023)
- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines](https://mlsanity.com/paper/2303.03132/) (2023)
- [Learning from Natural Language Explanations for Generalizable Entity Matching](https://mlsanity.com/paper/2406.09330/) (2024)
- [Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns](https://mlsanity.com/paper/2502.16813/) (2025)
- [KGLink: A column type annotation method that combines knowledge graph and pre-trained language model](https://mlsanity.com/paper/2406.00318/) (2024)
- [Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution](https://mlsanity.com/paper/2412.00324/) (2024)
- [MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching](https://mlsanity.com/paper/2308.01927/) (2023)
- [Towards Universal Dense Blocking for Entity Resolution](https://mlsanity.com/paper/2404.14831/) (2024)
- [TransClean: Finding False Positives in Multi-Source Entity Matching under Real-World Conditions via Transitive Consistency](https://mlsanity.com/paper/2506.04006/) (2025)
- [Entity Resolution via Batched Oracle Queries](https://mlsanity.com/paper/2606.24407/) (2026)
- [APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching](https://mlsanity.com/paper/2405.04820/) (2024)
- [GraLMatch: Matching Groups of Entities with Graphs and Language Models](https://mlsanity.com/paper/2406.15015/) (2024)
- [Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach](https://mlsanity.com/paper/2502.10453/) (2025)
- [LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes](https://mlsanity.com/paper/2602.08793/) (2026)
- [Alignment-Guided Largest Table Overlap Size Estimation](https://mlsanity.com/paper/2607.03049/) (2026)
- [Beyond Scale and Generation: Understanding Language Model-based Entity Matching](https://mlsanity.com/paper/2607.24688/) (2026)

## Abstract (arXiv)

Machine learning (ML) is playing an increasingly important role in data management tasks, particularly in Data Integration and Preparation (DI&P). The success of ML-based approaches, however, heavily relies on the availability of large-scale, high-quality labeled datasets for different tasks. Moreover, the wide variety of DI&P tasks and pipelines oftentimes requires customizing ML solutions which can incur a significant cost for model engineering and experimentation. These factors inevitably hold back the adoption of ML-based approaches to new domains and tasks. In this paper, we propose Sudowoodo, a multi-purpose DI&P framework based on contrastive representation learning. Sudowoodo features a unified, matching-based problem definition capturing a wide range of DI&P tasks including Entity Matching (EM) in data integration, error correction in data cleaning, semantic type detection in data discovery, and more. Contrastive learning enables Sudowoodo to learn similarity-aware data representations from a large corpus of data items (e.g., entity entries, table columns) without using any labels. The learned representations can later be either directly used or facilitate fine-tuning with only a few labels to support different DI&P tasks. Our experiment results show that Sudowoodo achieves multiple state-of-the-art results on different levels of supervision and outperforms previous best specialized blocking or matching solutions for EM. Sudowoodo also achieves promising results in data cleaning and semantic type detection tasks showing its versatility in DI&P applications.