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

2026-04-10

AI Summary

Traditional approaches rely on manually designed annotation schemas and exhaustive document labeling, which are costly and difficult to scale. This work proposes an end-to-end framework leveraging large language models to automatically transform natural language research questions and raw text into structured databases, supported by an interactive interface that enables user-guided refinement. The method establishes, for the first time, a closed-loop pipeline from research questions to structured evidence, integrating expert feedback and domain-adaptation mechanisms. Evaluated in legal and computational biology domains, it significantly enhances the efficiency and accuracy of cross-domain information extraction. The system, along with its public web interface, has been open-sourced.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

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

Problem

• structured data
• annotation schema
• document collections
• research question
• evidence extraction

Innovation

• interactive schema discovery
• large language models
• structured data extraction
• domain expert collaboration
• open-source framework