LLM-Driven Multimodal Knowledge Graph Construction for Industrial Process With Prompt Optimization and Fuzzy RAG

The industrial flotation process involves multimodal data and cross-procedural knowledge, presenting significant challenges for knowledge system management and traditional knowledge graph (KG) construction methods. This study develops a large language model (LLM)-driven framework to construct a domain-specific multimodal KG. The Flotation Knowledge Tree ontology is designed as the structural backbone of the KG to organize multi-level operations and interactions while integrating heterogeneous flotation data. To enhance the LLM's domain adaptability, adaptive prompt optimization is proposed to iteratively refine the extraction template with flotation-specific examples and performance feedback, enabling more accurate triple extraction. Additionally, a fuzzy information entropy-driven Retrieval-Augmented Generation (RAG) method is proposed, leveraging fuzzy logic to assign weights to key terms and numerical contexts to preserve causal relationships and semantic integrity under data uncertainty. Furthermore, we established a two-stage LLM-driven pipeline to generate initial triples using a LoRA fine-tuned lightweight LLM with the optimized prompt and fuzzy RAG context, followed by refinement with a large-scale LLM for precision and ontology alignment. Validated triples are mapped onto the Flotation Knowledge Tree to form a structured, high-quality KG with minimal manual intervention. This framework fuses ontology structuring, fuzzy semantic retrieval, and LLM reasoning to enable automated, domain-tailored knowledge assembly for industrial flotation processes. The constructed KG is evaluated using the Flotation Knowledge Tree-based method, achieving a score of 0.92, with a structural score of 0.97 and a semantic score of 0.88, demonstrating robustness and coherence.