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English(EN) Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models

AI框架利用知识图谱和大型语言模型自动化工程安全规范

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新框架，该框架结合使用知识图谱（KG）和受限的大型语言模型（LLM）来自动化工程规范中因果关系（C&E）逻辑的创建。该方法旨在减少生成安全联锁、报警合理化表和因果关系矩阵时通常伴随的手动工作和不一致性。该系统在机器可解释的知识图谱中表示过程信息、故障和缓解措施，然后由LLM将其转换为操作员可读的安全说明和SWRL规则，确保输出基于底层的语义模型。 AI

影响 该框架可以显著简化安全关键工程文档的创建过程，减少过程控制和安全系统中的错误和人工劳动。

排序理由 该集群包含一篇学术论文，详细介绍了用于自动化工程规范的新AI框架。

在 arXiv cs.AI 阅读 →

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AI框架利用知识图谱和大型语言模型自动化工程安全规范

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该集群包含一篇学术论文，详细介绍了用于自动化工程规范的新AI框架。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Javal Vyas, Milapji Singh Gill, Mehmet Mercang\"oz ·

    使用知识图谱和大型语言模型自动化因果关系规范

    arXiv:2606.31614v1 Announce Type: cross Abstract: Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&amp;E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, …

  2. arXiv cs.AI TIER_1 English(EN) · Mehmet Mercangöz ·

    使用知识图谱和大型语言模型自动化因果关系规范

    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 sema…

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