OmniMatch：表格数据存储库中有效的自监督任意连接发现
OmniMatch: Effective Self-Supervised Any-Join Discovery in Tabular Data Repositories
AI总结：
针对元数据缺失下表格列连接发现的难题，提出结合列对相似性度量与GNN的OmniMatch技术，利用图传递性提升召回率，自动化负例生成提高精确率，在F1和AUC上较现有方法提升最高达14%，无需依赖元数据与用户设定阈值。
摘要：
我们如何发现数据存储库中表格数据各列之间的连接关系？在元数据缺失的情况下能否有效完成这一任务？传统的列匹配方法主要依赖基于精确值重叠的相似性度量，因此会遗漏重要语义，或无法处理数据中的噪声。与此同时，近期聚焦于深度表表示学习技术的数据集发现方法，并未考虑到现有匹配与发现方法中存在的丰富列相似性信号。最后，现有方法严重依赖用户提供的相似性阈值，阻碍了其在实际场景中的部署。本文提出OmniMatch，这是一种新型连接发现技术，通过将列对相似性度量与图神经网络（GNNs）相结合，检测列之间的等值连接和模糊连接。OmniMatch的GNN能够利用图的传递性捕捉列之间的关联性，显著提升连接发现任务的召回率。同时，OmniMatch还通过自动化负例生成流程，用负的列连接样本扩充训练数据，从而提高了精确率。最重要的是，与最先进的匹配和发现方法相比，OmniMatch在F1分数和AUC上的效果提升高达14%，且无需依赖元数据或用户为每个相似性指标提供阈值。
英文摘要：
How can we discover join relationships among columns of tabular data in a data repository? Can this be done effectively when metadata is missing? Traditional column matching works mainly rely on similarity measures based on exact value overlaps, hence missing important semantics or failing to handle noise in the data. At the same time, recent dataset discovery methods focusing on deep table representation learning techniques, do not take into consideration the rich set of column similarity signals found in prior matching and discovery methods. Finally, existing methods heavily depend on user-provided similarity thresholds, hindering their deployability in real-world settings. In this paper, we propose OmniMatch, a novel join discovery technique that detects equi-joins and fuzzy-joins betwen columns by combining column-pair similarity measures with Graph Neural Networks (GNNs). OmniMatch's GNN can capture column relatedness leveraging graph transitivity, significantly improving the recall of join discovery tasks. At the same time, OmniMatch also increases the precision by augmenting its training data with negative column join examples through an automated negative example generation process. Most importantly, compared to the state-of-the-art matching and discovery methods, OmniMatch exhibits up to 14% higher effectiveness in F1 score and AUC without relying on metadata or user-provided thresholds for each similarity metric.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
