KMagent: A Multi-agent Large Language Model-Based Knowledge Management Platform for Product Innovation
Year: 2025
Type: conference-paper
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.
DOI: 10.57711/e8x6-a739
Author: Osei-Brefo E, Bhardwaj M, Liang H, Zhang Y, Scott S, Qayyam Z
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7087079809
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