Beyond Vector Similarity: A Structural Analysis of Graph-Augmented Retrieval for Industrial Knowledge Graphs
arXiv:2606.06003v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) fails systematically on queries requiring structural reasoning over interconnected entities. We compare eight retrieval architectures for aerospace supply chain intelligence, progressing from text retrieval through graph traversal to graph computation. Using a 46-node knowledge graph with 64 typed edges, we evaluate 23 queries across 10 intent categories and demonstrate that five query classes are structurally unreachable for vector retrieval. Our central finding is the operator vocabulary thesis: the barrier
The increasing adoption of RAG systems for complex enterprise data highlights their limitations, making advanced retrieval techniques a critical area of research and development.
This research provides a foundational understanding of RAG's structural limits and points towards alternative, more robust methods for knowledge graph integration, crucial for sophisticated AI applications.
The understanding of RAG's capabilities shifts from a universal solution to one requiring structural enhancements for complex reasoning, necessitating new architectural approaches for AI systems.
- · Expert systems developers
- · Graph database providers
- · Enterprises with complex knowledge graphs
- · Vector similarity search-only vendors
- · Generic RAG solution providers
AI systems will adopt hybrid retrieval methods combining vector search with graph traversal and computation.
This will drive the development of new tooling and frameworks specifically designed for structural reasoning over knowledge graphs in AI applications.
The ability of AI to perform complex, multi-hop reasoning over enterprise data will significantly improve, leading to more reliable and powerful AI agents.
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Read at arXiv cs.AI