Integrating Ontologies with Large Language Models for Enhanced Control Systems in Chemical Engineering

TL;DR

This paper introduces an ontology-integrated LLM framework for chemical engineering that combines structured domain knowledge with generative reasoning to improve control systems, safety analysis, and interpretability.

Contribution

It presents a novel pipeline that aligns LLM training and inference with the COPE ontology, integrating symbolic knowledge with neural models for enhanced control system applications.

Findings

Improved factual accuracy through ontology-guided decoding

Enhanced interpretability with structured semantic prompts

Quantitative evaluation shows better ontological consistency

Abstract

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.