# Scientific Table Search Using Keyword Queries

arXiv: https://arxiv.org/abs/1707.03423 · HTML: https://mlsanity.com/paper/1707.03423/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry](https://mlsanity.com/paper/2106.12944/) (2021)
- [ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models](https://mlsanity.com/paper/2410.22360/) (2024)
- [Enhancing Table Representations with LLM-powered Synthetic Data Generation](https://mlsanity.com/paper/2411.03356/) (2024)
- [REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources](https://mlsanity.com/paper/2504.07584/) (2025)
- [SAVeD: Semantic Aware Version Discovery](https://mlsanity.com/paper/2511.17298/) (2025)
- [FollowTable: A Benchmark for Instruction-Following Table Retrieval](https://mlsanity.com/paper/2605.00400/) (2026)

## Abstract (arXiv)

Tables are common and important in scientific documents, yet most text-based document search systems do not capture structures and semantics specific to tables. How to bridge different types of mismatch between keywords queries and scientific tables and what influences ranking quality needs to be carefully investigated. This paper considers the structure of tables and gives different emphasis to table components. On the query side, thanks to external knowledge such as knowledge bases and ontologies, key concepts are extracted and used to build structured queries, and target quantity types are identified and used to expand original queries. A probabilistic framework is proposed to incorporate structural and semantic information from both query and table sides. We also construct and release TableArXiv, a high quality dataset with 105 queries and corresponding relevance judgements for scientific table search. Experiments demonstrate significantly higher accuracy overall and at the top of the rankings than several baseline methods.