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KMagent: A Multi-agent Large Language Model-Based Knowledge Management Platform for Product Innovation

Lookup NU author(s): Dr EMMANUEL OSEI BREFO, Manish Bhardwaj, Dr Huizhi LiangORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

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.

Publication metadata

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

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