发表机构
ISI Foundation; European Commission, Joint Research Centre (JRC); UNICEF（ISI基金会; 欧盟委员会联合研究中心; 联合国儿童基金会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出结合EM-DAT等异构数据源的检索增强生成流程，生成可溯源的灾难故事情节与因果知识图谱，经评估其检索精度高、因果关系忠实，获专家偏好，拟扩展至全EM-DAT目录并公开叙事增强版本。
AI 中文摘要
有效的人道主义响应依赖于异构、海量信息源的快速合成，这项任务在危机的关键早期阶段往往超出人类的分析能力。我们提出一种流程，将来自EM-DAT的结构化灾难记录与来自ReliefWeb和欧洲媒体监测（EMM）的非结构化文档相结合，生成基于来源的灾难故事情节和因果知识图谱，为响应者和分析人员提供态势感知支持。该流程利用检索增强生成（RAG）提取结构化故事情节——涵盖17个字段的表格化事件概况，从严重程度、关键驱动因素到对儿童敏感的影响指标，并构建因果知识图谱，其中每个节点和边都配有基于引文的解释性叙述，可完整追溯至原始来源。我们通过包含9名领域专家和9名非专家评估人员的人工评估，在3种不同的危机用例上对系统进行评估。结果证实了较高的检索精度、提取因果关系的强忠实度，且专家明显偏好基于引文的组件而非无依据的替代方案。该流程旨在扩展至完整的EM-DAT目录，目标是公开发布该数据库的叙事增强版本。
英文摘要
Effective humanitarian response depends on the rapid synthesis of heterogeneous, high-volume information sources - a task that routinely exceeds human analytical capacity in the critical early hours of a crisis. We present a pipeline that combines structured disaster records from EM-DAT with unstructured documents from ReliefWeb and the European Media Monitor (EMM) to produce source-grounded disaster storylines and causal knowledge graphs supporting situational awareness for responders and analysts. Using Retrieval-Augmented Generation, the pipeline extracts structured storylines - tabular event profiles covering 17 fields, from severity and key drivers to child-sensitive impact indicators - and constructs causal knowledge graphs where each node and edge is enriched with citation-grounded explanatory narratives, enabling full traceability back to primary sources. We evaluate the system on three diverse crisis use cases through a human evaluation involving 9 domain expert and 9 non-expert evaluators. Results confirm high retrieval precision, strong faithfulness of extracted causal relations, and a clear expert preference for citation-grounded components over ungrounded alternatives. The pipeline is designed to scale to the full EM-DAT catalogue, with the goal of publicly releasing a new collection of disaster stories complementing the EM-DAT database.
Journal refInternational Conference on Information Technology for Social Good (GoodIT '26), September 02--04, 2026, Pisa, Italy
