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Augmenting Orbital Debris Identification with Neo4j-Enabled Graph-Based Retrieval-Augmented Generation for Multimodal

Daniel S Roll1, Zeyneb Kurt2, Yulei Li1

  • 1Department of Mathematics, Physics and Electrical Engineering, Faculty of Engineering and Environment, Northumbria University, Newcastle City Campus, College Street, Newcastle-upon-Tyne NE1 8ST, UK.

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|September 19, 2025
PubMed
Summary
This summary is machine-generated.

A new Graph-based Retrieval-Augmented Generation (GraphRAG) system combines Large Language and Vision Assistant (LLaVA) with Neo4j graph databases. This approach enhances AI accuracy in orbital debris detection by retrieving structured knowledge, reducing AI hallucinations.

Keywords:
graph databasesknowledge retrievallarge language modelsorbital debrisretrieval-augmented generationspace situational awareness

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Area of Science:

  • Artificial Intelligence
  • Knowledge Representation
  • Computer Vision

Background:

  • Current intelligent methods for orbital debris detection can be improved with advanced AI.
  • Large Language Models (LLMs) offer powerful text processing but can hallucinate information.
  • Integrating structured knowledge retrieval can enhance LLM factual accuracy.

Purpose of the Study:

  • To develop and evaluate a Graph-based Retrieval-Augmented Generation (GraphRAG) system.
  • To enhance the output quality of multimodal LLMs, specifically LLaVA, using structured knowledge.
  • To support orbital debris detection by improving AI-driven information retrieval and accuracy.

Main Methods:

  • Construction of a GraphRAG system integrating LLaVA with Neo4j graph database software.
  • Extraction, summarization, and embedding of research papers into a Neo4j graph.
  • API-powered LLM-generated relationships to enrich graph interconnections.
  • Contextual document retrieval and prompt engineering for LLM inference.

Main Results:

  • Qualitative results show successful information retrieval using the GraphRAG system.
  • The system demonstrated a reduction in LLM hallucinations when processing orbital debris data.
  • Integration of external information and graph-based retrieval improved qualitative output.

Conclusions:

  • The GraphRAG system offers a scalable and interpretable method for domain-specific knowledge retrieval.
  • This approach enhances the qualitative accuracy of LLM outputs for description-based tasks.
  • Further refinement and quantitative benchmarking are necessary to fully assess performance.