Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
摘要
Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency. This paper presents a semantic-AI framework that automates the generation of C&E logic by combining a knowledge graph (KG) with a constrained large language model (LLM) layer. The KG builds on an established modular alignment ontology to represent process structure, operating modes, faults, symptoms, causes, and mitigation actions in a machine-interpretable form. The LLM then transforms this information into operator-ready safety narratives and Semantic Web Rule Language (SWRL) rules under strict ontology and vocabulary constraints, grounding the generated artifacts in the underlying semantic model. The workflow is demonstrated on a modular process plant, showing how engineering semantics, diagnostic relations, and machine-verifiable specifications can be generated from a unified knowledge representation with reduced manual effort.
引用
@article{arxiv.2606.31614,
title = {Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models},
author = {Javal Vyas and Milapji Singh Gill and Mehmet Mercangöz},
journal= {arXiv preprint arXiv:2606.31614},
year = {2026}
}