用多智能体大模型让普通人也能自然语言查询地理数据。
Towards a Barrier-free GeoQA Portal: Natural Language Interaction with Geospatial Data Using Multi-Agent LLMs and Semantic Search
- 拆解复杂地理查询为子任务，由专业智能体协作处理。
- 支持自定义数据输入，语义搜索提升模糊查询准确率。
- 适合非专业人士使用，提升地理信息门户的易用性。
地理信息门户对获取和分析地理空间数据至关重要，推动开放空间数据共享与在线地理信息服务。尽管设计上具备类似GIS的交互与分层可视化功能，但复杂的操作和重叠图层常使非专业人士难以理解空间关系。本文提出一种基于多智能体大语言模型的GeoQA门户，实现地理数据的无缝自然语言交互。复杂查询被分解为子任务，由专用智能体协同执行，高效检索相关地理数据，并向用户展示任务规划过程，增强透明度。门户支持默认与自定义数据输入，灵活性高。通过词向量相似性进行语义搜索，即使查询术语不精确也能有效召回数据。案例研究、评估及用户测试验证了其在非专业人士中的有效性，成功弥合了GIS复杂性与公众访问之间的鸿沟，为未来地理信息门户提供直观解决方案。
原文摘要 · Abstract (English)
A Barrier-Free GeoQA Portal: Enhancing Geospatial Data Accessibility with a Multi-Agent LLM Framework Geoportals are vital for accessing and analyzing geospatial data, promoting open spatial data sharing and online geo-information management. Designed with GIS-like interaction and layered visualization, they often challenge non-expert users with complex functionalities and overlapping layers that obscure spatial relationships. We propose a GeoQA Portal using a multi-agent Large Language Model framework for seamless natural language interaction with geospatial data. Complex queries are broken into subtasks handled by specialized agents, retrieving relevant geographic data efficiently. Task plans are shown to users, boosting transparency. The portal supports default and custom data inputs for flexibility. Semantic search via word vector similarity aids data retrieval despite imperfect terms. Case studies, evaluations, and user tests confirm its effectiveness for non-experts, bridging GIS complexity and public access, and offering an intuitive solution for future geoportals.
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