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

TL;DR

This paper introduces a knowledge graph-driven multi-agent framework powered by large language models to enhance geospatial data discovery, addressing semantic heterogeneity and improving retrieval accuracy and transparency.

Contribution

It presents a novel semantic mediation layer and a multi-agent architecture that significantly improve geospatial data discovery over traditional keyword-based systems.

Findings

Enhanced intent matching accuracy

Improved ranking quality and recall

Greater discovery transparency

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 synthesis, forming an interpretable and closed-loop discovery process from user queries to results. Results from representative use cases and performance evaluation show that the framework substantially improves intent matching accuracy, ranking quality, recall, and discovery transparency compared with traditional systems. This study advances geospatial data discovery toward a more semantic, intent-aware, and intelligent paradigm, providing a practical foundation for next-generation intelligent and autonomous spatial data infrastructures and contributing to the broader vision of Autonomous GIS.