CoMA-IKG: LLM-Driven Multiagent Framework for Automated Construction of Industrial Knowledge Graph

With the continuous expansion of industrial systems, multisource and heterogeneous industrial data have increased rapidly, making the construction of a structured industrial knowledge system a core requirement in the industrial domain. Industrial knowledge graph (IKG) serves as a key approach for knowledge structuring and relation modeling and has become an indispensable foundation for industrial tasks. However, existing IKG construction methods still face core challenges such as data heterogeneity, complex semantic understanding, frequent knowledge changes, and limited automation. Inspired by the construction of IKG by industry experts, we propose CoMA-IKG, an large language model (LLM)-driven collaborative multiagent framework for automated construction of IKG. In the industrial data processing stage, an LLM-driven adaptive chunking agent is developed to achieve semantically complete and self-adjusting segmentation. In the triple extraction stage, a cluster of LLM-driven agents for progressive triple reasoning extraction and mechanism-aware logical discrimination is constructed to enable accurate industrial triple extraction under stepwise reasoning and industrial mechanism constraints. In the IKG evolution stage, an LLM-driven co-evolution agent is developed to generate evolution commands automatically based on the structural state of the IKG and real-time industrial data changes, enabling autonomous updating and continuous evolution of the IKG. Experimental results show that CoMA-IKG significantly outperforms existing automated knowledge graph construction methods in terms of relation mining, logical reasoning, and dynamic evolution of the IKG.