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

TL;DR

Sudowoodo is a contrastive self-supervised learning framework that unifies multiple data integration and preparation tasks, reducing the need for labeled data and enabling versatile, high-performance solutions across various DI&P applications.

Contribution

The paper introduces Sudowoodo, a novel contrastive learning framework that unifies diverse DI&P tasks under a single problem definition, enabling label-efficient, adaptable data representations.

Findings

Achieves state-of-the-art results in entity matching with minimal supervision

Outperforms specialized solutions in data cleaning and semantic type detection

Demonstrates high versatility across multiple DI&P tasks

Abstract

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.