Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
For engineers in process control and safety, this work automates the manual, error-prone creation of cause-and-effect specifications, though it is demonstrated on a single plant and lacks quantitative comparison.
The paper presents a framework combining knowledge graphs and large language models to automate the generation of cause-and-effect logic for process control and safety, demonstrating reduced manual effort on a modular process plant.
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.