Automated Knowledge Graph Construction for Supply Chain Datasets Assisted by LLMs

In today’s data-driven supply chain management landscape, establishing connections among stakeholders from diverse resources is essential for effective analysis and decision-making. Knowledge graphs (KGs) provide a transformative solution by organizing fragmented inventory data into semantically rich, interconnected networks, enabling contextualized insights and robust reasoning. However, automating KG construction for supply chain datasets is challenging due to issues such as heterogeneous data integration (e.g., text documents, spreadsheets), domain-specific contextualization, and the need to model implicit operational dependencies. This paper introduces a novel framework that leverages large-language models (LLMs) with multi-step prompting workflow to address these challenges. Our AutoKG4SC approach automates the extraction of entities from various sources and constructs KGs to capture complex interdependencies among these entities. We utilize zero-shot prompting for ontology construction, Named Entity Recognition (NER), and Relation Extraction (RE) tasks, thereby eliminating the need for extensive domain-specific training and human prior knowledge. We validate the framework through a case study that demonstrates AutoKG4SC’s ability to construct high-quality KGs from supply chain datasets. This research presents an effective framework and prompt strategy for KG construction, which can be easily adapted to datasets with richer information and other application scenarios.