RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
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...
| Authors | Leonard Traeger, Enas Khwaileh, Andreas Behrend et al. |
|---|---|
| Primary category | cs.DB |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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