用知识图谱增强大模型，让化工控制更可靠
Integrating Ontologies with Large Language Models for Enhanced Control Systems in Chemical Engineering
- 将化工领域知识图谱融入大模型训练与推理流程
- 输出结果严格遵循知识图谱术语，提升准确性和可解释性
- 适合需要安全可控的工业场景，如过程控制与安全分析
本文提出一种融合知识图谱的大型语言模型框架，用于化工工程中的控制系统。通过数据获取、语义预处理、信息抽取和知识图谱映射等步骤，生成模板化的问答对以指导模型微调。采用面向控制的解码阶段和引用过滤机制，约束输出仅使用与知识图谱关联的术语，确保语法和事实准确性。评估指标同时衡量语言质量与知识图谱一致性。反馈机制与未来扩展（如语义检索、迭代验证）进一步提升系统可解释性与可靠性。该框架结合符号结构与神经生成，为过程控制、安全分析等关键工程场景提供透明可审计的LLM应用路径。
原文摘要 · Abstract (English)
This work presents an ontology-integrated large language model (LLM) framework for chemical engineering that unites structured domain knowledge with generative reasoning. The proposed pipeline aligns model training and inference with the COPE ontology through a sequence of data acquisition, semantic preprocessing, information extraction, and ontology mapping steps, producing templated question-answer pairs that guide fine-tuning. A control-focused decoding stage and citation gate enforce syntactic and factual grounding by constraining outputs to ontology-linked terms, while evaluation metrics quantify both linguistic quality and ontological accuracy. Feedback and future extensions, including semantic retrieval and iterative validation, further enhance the system's interpretability and reliability. This integration of symbolic structure and neural generation provides a transparent, auditable approach for applying LLMs to process control, safety analysis, and other critical engineering contexts.
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