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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.26898 |
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| _version_ | 1866917142179348480 |
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| author | Su, Crystal Yu, Kuai Zhang, Jingrui Shao, Mingyuan Bauer, Daniel |
| author_facet | Su, Crystal Yu, Kuai Zhang, Jingrui Shao, Mingyuan Bauer, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Ontologies with Large Language Models for Enhanced Control Systems in Chemical Engineering Su, Crystal Yu, Kuai Zhang, Jingrui Shao, Mingyuan Bauer, Daniel Machine Learning 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. |
| title | Integrating Ontologies with Large Language Models for Enhanced Control Systems in Chemical Engineering |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.26898 |