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Machine learning (ML) is playing an increasingly important role in datamanagement tasks, particularly in Data Integration and Preparation (DI&P). Thesuccess of ML-based approaches, however, heavily relies on the availability oflarge-scale, high-quality labeled datasets for different tasks. Moreover, thewide variety of DI&P tasks and pipelines oftentimes requires customizing MLsolutions which can incur a significant cost for model engineering andexperimentation. These factors inevitably hold back the adoption of ML-basedapproaches to new domains and tasks.  In this paper, we propose Sudowoodo, a multi-purpose DI&P framework based oncontrastive representation learning. Sudowoodo features a unified,matching-based problem definition capturing a wide range of DI&P tasksincluding Entity Matching (EM) in data integration, error correction in datacleaning, semantic type detection in data discovery, and more. Contrastivelearning enables Sudowoodo to learn similarity-aware data representations froma large corpus of data items (e.g., entity entries, table columns) withoutusing any labels. The learned representations can later be either directly usedor facilitate fine-tuning with only a few labels to support different DI&Ptasks. Our experiment results show that Sudowoodo achieves multiplestate-of-the-art results on different levels of supervision and outperformsprevious best specialized blocking or matching solutions for EM. Sudowoodo alsoachieves promising results in data cleaning and semantic type detection tasksshowing its versatility in DI&P applications.

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