用大模型把工程文本规则转为可自动验证的格式
Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL
- 用大模型将文本规则转为SHACL约束
- 在OWL本体上自动验证复杂建模规则
- 无需懂形式化方法也能检查工程规范
AutomationML（AML）实现了工程领域标准化数据交换，但现有建模建议多以非正式文本形式存在，无法在AML内部自动验证。本文提出一种流水线方法：首先通过RML和SPARQL将AML模型映射为OWL本体；其次利用大语言模型将文本规则转化为SHACL约束，并在生成的本体上进行验证；最后将验证结果自动转化为自然语言。该方法在一组AML推荐规则上进行了演示，结果显示即使复杂建模规则也可实现半自动化检查，用户无需掌握形式化方法或本体技术即可完成验证。
原文摘要 · Abstract (English)
AutomationML (AML) enables standardized data exchange in engineering, yet existing recommendations for proper AML modeling are typically formulated as informal and textual constraints. These constraints cannot be validated automatically within AML itself. This work-in-progress paper introduces a pipeline to formalize and verify such constraints. First, AML models are mapped to OWL ontologies via RML and SPARQL. In addition, a Large Language Model translates textual rules into SHACL constraints, which are then validated against the previously generated AML ontology. Finally, SHACL validation results are automatically interpreted in natural language. The approach is demonstrated on a sample AML recommendation. Results show that even complex modeling rules can be semi-automatically checked -- without requiring users to understand formal methods or ontology technologies.
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