@inproceedings{levy-etal-2026-schematiq,
    title = "{S}che{M}ati{Q}: From Research Question to Structured Data through Interactive Schema Discovery",
    author = "Levy, Shahar  and
      Habba, Eliya  and
      Mintz, Reshef  and
      Raveh, Barak  and
      Keydar, Renana  and
      Stanovsky, Gabriel",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.22/",
    pages = "220--230",
    ISBN = "979-8-89176-392-0",
    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."
}