构建灾害分析专用知识图谱，提升自然语言查表的准确率。
DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics
- 用专家知识图谱连接用户查询与地理数据库，明确概念与因果关系。
- 在75个测试查询上，性能优于现有方法1.4至2.75倍，最高达3.56分（满分5）。
- 适合需要跨表推理和地理空间理解的灾害应急研究者使用。
灾害不可避免且代价高昂，有效响应依赖于结构化表格数据：涵盖灾害、暴露、脆弱性和生命线基础设施的精确、信息密集记录。现有文本转SQL方法在灾难领域迁移效果差，因查询涉及异构地理空间模式并需因果推理。我们提出DisasterLex，一种基于知识图谱的框架，通过专家知识图谱（EKG）将用户查询与数据库间的关键概念及有类型因果边关联，并通过概念到表的链接对接模式。该框架分四阶段运行（识别查询实体、路由至业务域、规划因果边、生成SQL），每步限制传递给模型的模式范围。我们在一个包含36张地理空间表、150列的灾害分析数据库上实现，配备107个概念、117条因果边和52个概念-模式链接，评估集含75个查询。在七个基础模型（涵盖专有与开源系列）上，DisasterLex超越四种顶尖基线（LightRAG、HippoRAG 2、ReFoRCE、CHESS），性能提升1.4倍至2.75倍，绝对得分达1.65至3.56（满分5）。错误分析显示，基线失败集中于路由与多表SQL构造，而这些正是我们架构显式处理的环节。代码、数据及EKG资源已发布于https://github.com/YimingXiao98/DisasterLex 及 Zenodo https://doi.org/10.5281/zenodo.20388029。
原文摘要 · Abstract (English)
Disasters are inevitable and increasingly costly, and effective response depends on querying structured tabular data: precise, information-dense records of hazard, exposure, vulnerability, and lifeline infrastructure that underpin disaster management. Current text-to-SQL methods enable natural-language access to such tables but transfer poorly to the disaster domain, where queries span heterogeneous geospatial schemas and require reasoning over causal relations. We introduce DisasterLex, a knowledge-graph-mediated framework that inserts an Expert Knowledge Graph (EKG) of curated concepts and typed causal edges between the user query and the database, bridged to schema by concept-to-table links. The orchestration runs four stages (identifying query entities, routing to the operational domain, planning over causal edges, and grounding the SQL), restricting the schema passed to the model at each step. We instantiate it on a disaster-analytics database (36 geospatial tables, 150 columns) with an EKG of 107 concepts, 117 causal edges, and 52 concept-to-schema links, evaluated on a 75-query test set. On all seven base models spanning proprietary and open-weight families, DisasterLex beats four state-of-the-art baselines (LightRAG, HippoRAG 2, ReFoRCE, CHESS) by 1.4x to 2.75x, with absolute scores of 1.65 to 3.56 (of 5.0). Error analysis shows baseline failures cluster in routing and multi-table SQL composition, the operations our orchestration explicitly addresses. Code, data, and the EKG artifact are available at https://github.com/YimingXiao98/DisasterLex and on Zenodo at https://doi.org/10.5281/zenodo.20388029.
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