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RACT introduces a self-supervised framework that leverages referential context to probabilistically retrieve candidate tables, effectively constraining the search space for multi-table schema matching. By filtering irrelevant columns through top-k table retrieval, the method significantly boosts matching precision and completeness over traditional similarity-based baselines. This approach addresses a key limitation in heterogeneous database integration where semantic similarity alone is insufficient for accurate column correspondence.
RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
arXiv:2606.07843v1 Announce Type: cross 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%.
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%.
Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
arXiv:2606.07843 [cs.DB]
(or arXiv:2606.07843v1 [cs.DB] for this version)
https://doi.org/10.48550/arXiv.2606.07843 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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