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

2026-03-21

AI Summary

This work addresses the limitations of existing geospatial data discovery systems, which rely primarily on keyword matching and lack semantic understanding, thereby struggling to accurately capture user intent. To overcome this, the authors propose a knowledge graph–based multi-agent framework that first constructs a unified geospatial metadata ontology as a semantic intermediary layer. Leveraging large language models, multiple agents collaboratively perform intent parsing, knowledge graph retrieval, and answer generation. This approach yields an interpretable, high-precision, and intent-aware data discovery mechanism. Experimental results demonstrate that, across representative use cases, the system significantly outperforms conventional methods in terms of intent-matching accuracy, ranking quality, recall, and discovery transparency.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• geospatial data discovery
• semantic inconsistency
• intent understanding
• heterogeneous metadata
• keyword-based search

Innovation

• knowledge graph
• multi-agent framework
• large language models
• geospatial data discovery
• semantic ontology