What is knowledge, and how do we measure it? Extracted knowledge is more than a true statement; it carries justification, provenance, and a context of use. When an expert builds a knowledge graph from scientific literature, every node and edge reflects a justified interpretation: they know why a relation holds, which paper supports it, and under what conditions it applies. This raises a fundamental challenge for AI-based information retrieval systems. Retrieval-augmented generation (RAG) can now produce knowledge graphs that resemble those assembled by hand, yet these graphs are generated through statistical pattern recognition rather than contextual understanding of the scientific processes they describe. The central question of this work is therefore: how can we measure whether such systems are epistemic agents or only convincing imitators of one?

To explore this problem, we developed OntoGeoRAG, an open-source framework that combines document retrieval, LLM-based information extraction, and entity–relation normalization to transform scientific PDFs into provenance-aware semantic knowledge graphs. Using mass-transport deposits in subsurface geophysics as a challenging test case, the framework achieves 77% recovery against an expert-curated benchmark. Rather than treating the remaining 23% as simple error, we interpret these failures as evidence of the current epistemic limitations of retrieval-based AI systems. Although demonstrated in geophysics, the methodology is domain-agnostic and transferable to any scientific field with structured terminology and complex conceptual relationships.