# Ad Hoc Table Retrieval using Semantic Similarity

arXiv: https://arxiv.org/abs/1802.06159 · HTML: https://mlsanity.com/paper/1802.06159/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 21 of 21)

- [AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry](https://mlsanity.com/paper/2106.12944/) (2021)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [Capturing Row and Column Semantics in Transformer Based Question Answering over Tables](https://mlsanity.com/paper/2104.08303/) (2021)
- [Retrieving Complex Tables with Multi-Granular Graph Representation Learning](https://mlsanity.com/paper/2105.01736/) (2021)
- [TabLib: A Dataset of 627M Tables with Context](https://mlsanity.com/paper/2310.07875/) (2023)
- [Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System](https://mlsanity.com/paper/2504.09207/) (2025)
- [Neural ranking models for document retrieval](https://mlsanity.com/paper/2102.11903/) (2021)
- [End-to-End Table Question Answering via Retrieval-Augmented Generation](https://mlsanity.com/paper/2203.16714/) (2022)
- [Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models](https://mlsanity.com/paper/2108.04049/) (2021)
- [Table Retrieval May Not Necessitate Table-specific Model Design](https://mlsanity.com/paper/2205.09843/) (2022)
- [CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering](https://mlsanity.com/paper/2106.04441/) (2021)
- [Enhancing Table Representations with LLM-powered Synthetic Data Generation](https://mlsanity.com/paper/2411.03356/) (2024)
- [Embeddings for Tabular Data: A Survey](https://mlsanity.com/paper/2302.11777/) (2023)
- [Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval](https://mlsanity.com/paper/2309.10506/) (2023)
- [BERT Meets Relational DB: Contextual Representations of Relational Databases](https://mlsanity.com/paper/2104.14914/) (2021)
- [Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning](https://mlsanity.com/paper/2603.19888/) (2026)
- [FollowTable: A Benchmark for Instruction-Following Table Retrieval](https://mlsanity.com/paper/2605.00400/) (2026)
- [PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries](https://mlsanity.com/paper/2605.18199/) (2026)
- [Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG](https://mlsanity.com/paper/2607.17742/) (2026)
- [Polaris: Learning to Generate Table Descriptions from Retrieval Feedback](https://mlsanity.com/paper/2608.17171/) (2026)
- [BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence](https://mlsanity.com/paper/2609.20886/) (2026)

## Abstract (arXiv)

We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline.