# SANTOS: Relationship-based Semantic Table Union Search

arXiv: https://arxiv.org/abs/2209.13589 · HTML: https://mlsanity.com/paper/2209.13589/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2022.

## Built on by (most cited first, 30 of 32)

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- [DALL-M: Context-Aware Clinical Data Augmentation with LLMs](https://mlsanity.com/paper/2407.08227/) (2024)
- [Generative Benchmark Creation for Table Union Search](https://mlsanity.com/paper/2308.03883/) (2023)
- [TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes](https://mlsanity.com/paper/2407.01619/) (2024)
- [Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks](https://mlsanity.com/paper/2505.21329/) (2025)
- [Humans, Machine Learning, and Language Models in Union: A Cognitive Study on Table Unionability](https://mlsanity.com/paper/2506.12990/) (2025)
- [RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching](https://mlsanity.com/paper/2606.07843/) (2026)
- [LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake](https://mlsanity.com/paper/2606.10460/) (2026)
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- [Conceptual Schema Inference for Tabular Datasets using Large Language Models](https://mlsanity.com/paper/2509.04632/) (2025)
- [Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data](https://mlsanity.com/paper/2602.02025/) (2026)
- [SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching](https://mlsanity.com/paper/2605.30729/) (2026)
- [Hyperdimensional computing for structured querying on tabular data embeddings](https://mlsanity.com/paper/2606.13871/) (2026)
- [Alignment-Guided Largest Table Overlap Size Estimation](https://mlsanity.com/paper/2607.03049/) (2026)

## Abstract (arXiv)

Existing techniques for unionable table search define unionability using metadata (tables must have the same or similar schemas) or column-based metrics (for example, the values in a table should be drawn from the same domain). In this work, we introduce the use of semantic relationships between pairs of columns in a table to improve the accuracy of union search. Consequently, we introduce a new notion of unionability that considers relationships between columns, together with the semantics of columns, in a principled way. To do so, we present two new methods to discover semantic relationship between pairs of columns. The first uses an existing knowledge base (KB), the second (which we call a "synthesized KB") uses knowledge from the data lake itself. We adopt an existing Table Union Search benchmark and present new (open) benchmarks that represent small and large real data lakes. We show that our new unionability search algorithm, called SANTOS, outperforms a state-of-the-art union search that uses a wide variety of column-based semantics, including word embeddings and regular expressions. We show empirically that our synthesized KB improves the accuracy of union search by representing relationship semantics that may not be contained in an available KB. This result hints at a promising future of creating a synthesized KBs from data lakes with limited KB coverage and using them for union search.