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 retrieva

Source: arXiv cs.LG — read the full report at the original publisher.

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