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    <abstract>Many disciplines pose natural-language research questions over large document collections whose answers typically requires 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.</abstract>
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%0 Conference Proceedings
%T ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery
%A Levy, Shahar
%A Habba, Eliya
%A Mintz, Reshef
%A Raveh, Barak
%A Keydar, Renana
%A Stanovsky, Gabriel
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F levy-etal-2026-schematiq
%X Many disciplines pose natural-language research questions over large document collections whose answers typically requires 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.
%U https://aclanthology.org/2026.acl-demo.22/
%P 220-230

Markdown (Informal)

[ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery](https://aclanthology.org/2026.acl-demo.22/) (Levy et al., ACL 2026)

• ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery (Levy et al., ACL 2026)

ACL

• Shahar Levy, Eliya Habba, Reshef Mintz, Barak Raveh, Renana Keydar, and Gabriel Stanovsky. 2026. ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 220–230, San Diego, California, United States. Association for Computational Linguistics.