# TabJoinBench: A Benchmark for Joinable Table Discovery

arXiv: https://arxiv.org/abs/2610.00817 · HTML: https://mlsanity.com/paper/2610.00817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [EmbeddingGemma: Powerful and Lightweight Text Representations](https://mlsanity.com/paper/2509.20354/) (2025)
- [GitTables: A Large-Scale Corpus of Relational Tables](https://mlsanity.com/paper/2106.07258/) (2021)
- [Dataset Discovery in Data Lakes](https://mlsanity.com/paper/2011.10427/) (2020)
- [Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach](https://mlsanity.com/paper/2010.13273/) (2020)
- [MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2506.05587/) (2025)
- [WarpGate: A Semantic Join Discovery System for Cloud Data Warehouses](https://mlsanity.com/paper/2212.14155/) (2022)
- [FREYJA: Efficient Join Discovery in Data Lakes](https://mlsanity.com/paper/2412.06637/) (2024)

## Abstract (arXiv)

Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstream tasks such as data exploration, feature engineering, and business intelligence. Although numerous join discovery methods have been proposed, existing studies rely on method-specific benchmark construction, making reproducible and fair comparison difficult. We present TabJoinBench, a benchmark for evaluating join discovery methods across semantic, relational, and hybrid data lake scenarios. TabJoinBench constructs query-candidate pairs using source-specific validation strategies, systematically introduces structural, representation, and semantic changes through composable perturbations while preserving reliable ground truth. We evaluate representative join discovery methods spanning set-based, feature-based, and learned approaches, together with general-purpose language-model embedding baselines, and publicly release the processed datasets, ground-truth annotations, and generation pipeline to facilitate reproducible evaluation and future research.