# ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery

arXiv: https://arxiv.org/abs/2604.09237 · HTML: https://mlsanity.com/paper/2604.09237/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning](https://mlsanity.com/paper/2409.12183/) (2024)
- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)
- [Instruct and Extract: Instruction Tuning for On-Demand Information Extraction](https://mlsanity.com/paper/2310.16040/) (2023)
- [We Should Evaluate Real-World Impact](https://mlsanity.com/paper/2507.05973/) (2025)
- [Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Datasets](https://mlsanity.com/paper/2406.05348/) (2024)
- [Intent-Aware Schema Generation And Refinement For Literature Review Tables](https://mlsanity.com/paper/2507.19521/) (2025)

## Built on by (most cited first, 3 of 3)

- [Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research](https://mlsanity.com/paper/2609.22209/) (2026)
- [EviStreams: Human-in-the-Loop AI Data Extraction for Systematic Reviews in Medicine](https://mlsanity.com/paper/2609.27418/) (2026)
- [TRACE: Target-Aware Retrieval, Attributed Evidence, and Contract-Constrained Extraction for LitTraceQA](https://mlsanity.com/paper/2609.38861/) (2026)

## Abstract (arXiv)

Many disciplines pose natural-language research questions over large document collections whose answers typically require structured evidence, traditionally obtained by manually designing an annotation schema and exhaustively labeling the corpus, a slow and error-prone process. We introduce ScheMatiQ, which leverages calls to a backbone LLM to take a question and a corpus to produce a schema and a grounded database, with a web interface that lets steer and revise the extraction. In collaboration with domain experts, we show that ScheMatiQ yields outputs that support real-world analysis in law and computational biology. We release ScheMatiQ as open source with a public web interface, and invite experts across disciplines to use it with their own data. All resources, including the website, source code, and demonstration video, are available at: www.ScheMatiQ-ai.com