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Lookup NU author(s): Dr EMMANUEL OSEI BREFO, Manish Bhardwaj, Dr Huizhi LiangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper presents KMagent, a multi-agent Large Language Model-based knowledge management platform designed to accelerate product innovation within industrial Research \& Development environments. The system integrates structured Knowledge Graph construction, LLM-driven Question Answering, and a comprehensive evaluation framework under a Retrieval-Augmented Generation architecture. By combining LLM reasoning with domain-specific KGs, KMagent produces accurate and context-aware responses to complex queries. To assess performance, we introduce a dual-layered evaluation framework covering both the structural integrity of the KG and the factual quality of generated responses using reference-based metrics such as BLEU, ROUGE, METEOR, BERTScore and reference-free metrics such as UniEval and G-Eval. Using polymer degradation as a case study, we demonstrate the system's effectiveness across knowledge extraction, reasoning, and evaluation.
Author(s): Osei-Brefo E, Bhardwaj M, Liang H, Zhang Y, Scott S, Qayyam Z
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: The 11th Annual Conference on machine Learning, Optimization and Data science (LOD)
Year of Conference: 2025
Online publication date: 24/09/2025
Acceptance date: 15/06/2025
Date deposited: 13/08/2025
URL: https://lod2025.icas.events/
ePrints DOI: 10.57711/e8x6-a739
