Design and Implementation of a Semiconductor Supply Chain Knowledge Graph for Developing a GraphRAG-Based Question Answering Model

A knowledge graph (KG) supports effective knowledge extraction and management, driving active research in supply chain management (SCM). This study constructs a KG representing the semiconductor manufacturing supply chain and develops a GraphRAG-based question answering (QA) model. Representing such processes as a KG requires domain-specific ontology design, which is typically manual, time-consuming, and costly. To address this, we adapt an existing ontology from the automotive manufacturing domain to the semiconductor context. The QA model integrates the domain ontology into Chain-of-Thought (CoT) prompts to generate accurate responses to complex queries. This approach improves knowledge retrieval efficiency, mitigates the context-length limitations of a large language model (LLM), and enables interpretable answers by exposing the model’s reasoning process. We quantitatively evaluate the proposed GraphRAG model against the VectorRAG baseline, demonstrating superior performance in BERTScore and F1-score metrics. This framework highlights the benefits of combining ontological knowledge and CoT prompting for enhancing graph-based QA systems.