Knowledge graph-based retrieval-augmented generation on enterprise knowledge graphs
Abstract
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge sources at inference time to improve factual accuracy and reduce hallucination. Recent work has explored Knowledge Graph-based RAG (KG-RAG) to enable structured retrieval and complex reasoning. However, most existing methods are evaluated on open-domain Knowledge Graphs (KGs) and rely on assumptions-such as sparse connectivity and clean schema-that do not hold in industrial settings. In dense, schema-driven enterprise KGs, these assumptions break down, causing uncontrolled search expansion and unreliable reasoning behavior that severely degrades answer quality, thereby limiting the reliability and deployability of existing KG-RAG systems in real-world industrial applications. This gap is critical because enterprise question answering often supports operational, maintenance, and diagnostic decisions, where unreliable reasoning can lead to incorrect conclusions and costly downstream consequences. This thesis investigates KG-RAG in the context of a real-world enterprise KG derived from Bosch’s Configuration Management Database (CMDB) as a representative industrial case study, while targeting challenges common across enterprise KGs. We construct an evaluation benchmark based on business-relevant questions drawn from real enterprise practice, ensuring that the queries reflect actual information needs rather than synthetic or open-domain benchmarks. Our empirical analysis…
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