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.

翻译：机器学习(ML)在数据管理任务中正在发挥越来越重要的作用,特别是在数据整合和准备(DI&P)方面。但是,基于ML方法的成功在很大程度上依赖于提供大规模、高品质的标签化不同任务数据集。此外,由于DI&P任务和管道的种类繁多,往往需要定制ML解决方案,为模型工程和实验带来巨大的成本。这些因素不可避免地阻碍对新领域和任务采用基于ML的方法。在本文中,我们提议 Sudowoodo,一个基于对比性代表制学习的多功能的DIP框架。 Sudowoodo 具有一个统一、匹配的基于问题的定义,在数据整合、数据清理错误校正、数据发现中的语义型检测等多个任务中,包括实体匹配(EM)、数据清理、数据发现中的语义型检测等。对比性学习使Sudowoodo能够从大量数据项目(例如实体条目、表列)中学习相似的识别数据表示方式。在不使用任何标签的情况下,将多用途的识别和图示结果,以后,我们所学的演示演示的演示展示可以直接使用或演示式的多式任务,以显示其以往的模异的模化结果。