Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models

• URL: http://arxiv.org/abs/2606.31614v1
• Date: Tue, 30 Jun 2026 13:03:46 GMT
• Title: Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models
• Abstract summary: This paper presents a semantic-AI framework that automates the generation of C&E logic.<n>The framework combines a knowledge graph (KG) with a constrained large language model (LLM) layer.
• Score: 0.1223474232275842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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