Abstract
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.
Abstract (translated)
采购和识别新制造商合作伙伴对全球经济中的供应链多元化至关重要，这可以提高制造系统集成商的敏捷性，并通过供应链多元化提高风险降低。先进的大型语言模型的出现引起了广泛关注，因为它们能够生成全面且明确的回答，涵盖广泛的领域知识。然而，当回答领域特定问题时，系统往往存在准确性和完整性不足的情况，特别是在制造业服务发现领域。这项研究探讨了在知识图谱与 ChatGPT 的结合下，简化潜在客户在识别小制造企业过程中的可能性。 在本研究中，我们提出了一种方法，将自下而上的本体与先进机器学习模型相结合，从包括北美地区小型制造商的数字足迹在内的一系列结构和非结构化数据源中开发出制造业服务知识图。知识图和学到的图嵌入向量被用来处理数字供应链网络中的复杂查询，并回应提高可靠性和增强可解释性的答案。 所提出的方法具有可扩展性，可以将数百万实体分配到形成一个全球制造业服务知识网络图，这个网络图可能连接多个跨越行业部门、地理政治边界和企业领域的知识图。 为这项研究创建的数据集，现已成为公开可访问的数据库，包括13,000多个制造商网站、制造业服务、认证和位置实体类型。
URL
https://arxiv.org/abs/2404.06571
