Augmenting orbital debris identification with Neo4j-enabled graph-based retrieval-augmented generation for multimodal large language models

Roll, D.S. orcid.org/0009-0002-0927-1598, Kurt, Z. orcid.org/0000-0003-3186-8091, Li, Y. orcid.org/0000-0003-3579-6179 et al. (1 more author) (2025) Augmenting orbital debris identification with Neo4j-enabled graph-based retrieval-augmented generation for multimodal large language models. Sensors, 25 (11). p. 3352. ISSN 1424-8220

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Item Type: Article
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).

Keywords: large language models; retrieval-augmented generation; knowledge retrieval; graph databases; orbital debris; space situational awareness
Dates:
  • Submitted: 26 April 2025
  • Accepted: 22 May 2025
  • Published (online): 26 May 2025
  • Published: 1 June 2025
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 30 May 2025 15:18
Last Modified: 30 May 2025 15:18
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: 10.3390/s25113352
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