Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers’ weblinks, manufacturing services, certifications, and location entity types.

中文翻译：

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构建知识图来丰富制造服务发现中的 ChatGPT 响应

寻找和识别新的制造合作伙伴对于制造系统集成商通过全球经济中的供应链多元化来提高敏捷性并降低风险至关重要。先进的大语言模型的出现引起了人们的极大兴趣，因为它们能够在广泛的知识领域生成全面且清晰的响应。然而，在响应特定领域的查询时，特别是在制造服务发现等领域，该系统常常缺乏准确性和完整性。本研究探讨了利用知识图谱与 ChatGPT 结合的潜力，以简化潜在客户识别小型制造企业的流程。在这项研究中，我们提出了一种将自下而上的本体与先进的机器学习模型相结合的方法，从一系列结构化和非结构化数据源（包括整个北美小型制造商的数字足迹）开发制造服务知识图。利用知识图和学习的图嵌入向量来处理数字供应链网络中的复杂查询，以增强的可靠性和更大的可解释性进行响应。强调的方法可扩展到数百万个实体，这些实体可以分布形成全球制造服务知识网络图，该网络图可以潜在地互连跨越行业部门、地缘政治边界和业务领域的多种类型的知识图。为此研究开发的数据集现已公开，包含 13,000 多个制造商的网络链接、制造服务、认证和位置实体类型。