ScheMatiQ：通过交互式模式发现实现从研究问题到结构化数据的转化

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

April 10, 2026

作者: Shahar Levy, Eliya Habba, Reshef Mintz, Barak Raveh, Renana Keydar, Gabriel Stanovsky

cs.AI

摘要

许多学科领域都需要对大规模文献集进行自然语言研究提问，这类问题的解答通常需要结构化证据支撑。传统方法依赖人工设计标注框架并对语料库进行穷举式标注，这一过程不仅耗时且易出错。我们推出的ScheMatiQ系统，通过调用核心大语言模型，能够根据研究问题与语料库自动生成标注框架及基于证据的数据库，并配备可引导修正提取过程的网页交互界面。通过与领域专家合作，我们验证了ScheMatiQ在法律和计算生物学领域的实际分析价值。该系统现已作为开源项目发布，提供公开网页接口，诚邀各学科专家使用自有数据进行探索。所有资源（包括网站、源代码及演示视频）均可通过以下网址获取：www.ScheMatiQ-ai.com。

English

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