ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery
Shahar Levy, Eliya Habba, Reshef Mintz, Barak Raveh, Renana Keydar Hebrew University of Jerusalem, Gabriel Stanovsky Hebrew University of Jerusalem
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.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表