[2603.20670] Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models

Computer Science > Artificial Intelligence arXiv:2603.20670 (cs) [Submitted on 21 Mar 2026] Title:Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models Authors:Ruixiang Liu, Zhenlong Li, Ali Khosravi Kazazi View a PDF of the paper titled Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models, by Ruixiang Liu and 2 other authors View PDF Abstract:The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures still rely largely on keyword-based search with limited semantic support, which often fails to capture user intent and leads to weak retrieval performance. To address these challenges, this study proposes a knowledge graph-driven multi-agent framework for intelligent geospatial data discovery, powered by large language models. The framework introduces a unified geospatial metadata ontology as a semantic mediation layer to align heterogeneous metadata standards across platforms and constructs a geospatial metadata knowledge graph to explicitly model datasets and their multidimensional relationships. Building on the structured representation, the framework adopts a multi-agent collaborative architecture to perform intent parsing, knowledge graph retrieval, and answer...