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.

利用知识图谱和 ChatGPT 相结合，提出了一种整合自下而上本体论和先进机器学习模型的方法，从结构化和非结构化数据源中发展出制造服务知识图谱，用于解决数字供应链网络中复杂的查询，并具有更好的可靠性和可解释性。