Knowledge acquisition is a well-acknowledged bottleneck in the building of knowledge-based systems. Documents are a useful source of knowledge from experts. This paper targets the reuse of knowledge from the assembly phase of a product in the design and planning phases. Issues, their causes and the parameters involved are necessary to be acquired for reusing the knowledge so acquired. This paper discusses a method for knowledge acquisition, as a pipeline of existing tools in natural language understanding and processing. The acquired knowledge is expected to help in the decision making for a smart manufacturing system. The process of knowledge acquisition involves recognising the presence of issues and their causes using a combination of sentiment analysis and text patterns. The causes are then dissected to identify the constraints and constituent parameters. These pieces of knowledge are then reconstructed to form rules in a knowledge base. This paper demonstrates progress towards realising the method, by developing the cause dissection and rule-writing components, and validation of the issue-cause acquisition component with human subjects. A discussion is then presented on the potential integration and validation of the overall knowledge acquisition pipeline with a smart manufacturing system.

中文翻译：

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从自然语言文本到规则：从飞机装配的正式文件中获取知识

知识获取是构建基于知识的系统中公认的瓶颈。文档是来自专家的有用知识来源。本文的目标是在设计和规划阶段重用产品组装阶段的知识。为了重用所获得的知识，需要获取问题、问题的原因和所涉及的参数。本文讨论了一种知识获取方法，作为自然语言理解和处理中现有工具的管道。获得的知识有望帮助智能制造系统的决策。知识获取的过程涉及使用情感分析和文本模式的组合来识别问题的存在及其原因。然后剖析原因以识别约束和组成参数。然后重构这些知识片段以形成知识库中的规则。本文通过开发原因剖析和规则编写组件，以及用人类受试者验证问题原因获取组件，展示了实现该方法的进展。然后讨论了整个知识获取管道与智能制造系统的潜在集成和验证。