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

arXiv: https://arxiv.org/abs/2606.07843 · HTML: https://mlsanity.com/paper/2606.07843/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs](https://mlsanity.com/paper/2507.09477/) (2025)
- [Valentine: Evaluating Matching Techniques for Dataset Discovery](https://mlsanity.com/paper/2010.07386/) (2020)
- [SANTOS: Relationship-based Semantic Table Union Search](https://mlsanity.com/paper/2209.13589/) (2022)
- [Magneto: Combining Small and Large Language Models for Schema Matching](https://mlsanity.com/paper/2412.08194/) (2024)
- [PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching](https://mlsanity.com/paper/2109.07321/) (2021)
- [It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings](https://mlsanity.com/paper/2203.04366/) (2022)
- [LEAPME: Learning-based Property Matching with Embeddings](https://mlsanity.com/paper/2010.01951/) (2020)
- [FREYJA: Efficient Join Discovery in Data Lakes](https://mlsanity.com/paper/2412.06637/) (2024)
- [LLMATCH: A Unified Schema Matching Framework with Large Language Models](https://mlsanity.com/paper/2507.10897/) (2025)
- [Living in Parallel Realities -- Co-Existing Schema Versions with a Bidirectional Database Evolution Language](https://mlsanity.com/paper/1608.05564/) (2016)
- [SMUTF: Schema Matching Using Generative Tags and Hybrid Features](https://mlsanity.com/paper/2402.01685/) (2024)

## Built on by (most cited first, 1 of 1)

- [Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAG](https://mlsanity.com/paper/2607.22319/) (2026)

## Abstract (arXiv)

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%.