ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery
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
69dca35b2b6b8dc66ffeecc6
Shahar Levy
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2026-04-13T19:24:16.727Z
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Shahar Levy
Shahar6000
user
Shahar6000
69dca35b2b6b8dc66ffeecc7
Eliya Habba
69dca35b2b6b8dc66ffeecc8
Reshef Mintz
69dca35b2b6b8dc66ffeecc9
Barak Raveh
69dca35b2b6b8dc66ffeecca
Renana Keydar
69dca35b2b6b8dc66ffeeccb
Gabriel Stanovsky
ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery
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
ScheMatiQ uses large language model calls to automatically generate annotation schemas and structured databases from document collections, supporting domain-specific analysis in law and computational biology through an interactive web interface.
large language model annotation schema document collections structured evidence web interface domain experts real-world analysis open source public web interface