RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching

• URL: http://arxiv.org/abs/2606.07843v1
• Date: Fri, 05 Jun 2026 21:08:40 GMT
• Title: RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
• Abstract summary: RACT learning and prediction is used to exploit referential context for holistic schema matching.<n> constraining the column search space via top-t tables improves both average matching precision and completeness by up to +70%.
• Score: 0.13999481573773068
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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