通过检索候选表提升多表模式匹配精度，解决语义相似但上下文不同的列匹配难题。
RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
- 利用自监督框架检索源列的候选表，构建上下文约束
- 通过限制列搜索空间，匹配精度和完整率最高提升70%
- 适合处理异构设计下的跨表模式匹配任务
模式匹配是整合异源数据的关键任务，旨在识别不同模式中列之间的对应关系。在多表整体模式匹配中，语义相近的列可能因异构模式设计而分布在不同上下文的表中，基于相似性的方法难以应对。本文提出RACT学习与预测框架，通过引入参照上下文信息，在自监督机制下实现对源列候选表的概率性检索，从而约束相关列的候选范围。实验表明，该方法优于基于相似性的基线模型。后续匹配实验显示，通过top-t个候选表限制列搜索空间，平均匹配精度与完整性最高提升70%。
原文摘要 · Abstract (English)
Schema matching, a critical task for integrating data from diverse sources, seeks to identify correspondences between columns across different schemas. In multi-table holistic schema matching, columns with similar semantic meaning may reside in tables with different contexts due to heterogeneous schema designs, where similarity-based techniques are inadequate. The focus of this paper is exploiting referential context into schema matching by introducing RACT learning and prediction, a self-supervised framework enabling the probabilistic retrieval of candidate tables for source columns to constrain relevant column candidates. Experiments demonstrate that this approach outperforms similarity-based baselines on matching multi-table schemas. In subsequent matching experiments, constraining the column search space via top-t tables improves both average matching precision and completeness by up to +70%.
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