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End-to-End Graph Retrieval Pipeline for Specialized Domains

We present an end-to-end pipeline for constructing a domain-specific knowledge graph from instructional text using Large Language Model assisted extraction. Applied to the Icelandic Riding Levels, a 602 pages training corpus for riders of the Icelandic Horse, the pipeline produces a hyper-relational knowledge graph of 9,382 nodes and 16,423 edges, where schema-constrained qualifiers preserve the conditional and procedural context that standard triples discard. To evaluate the resulting graph, we introduce the first expert validated question answering benchmark for this domain: 252 questions across four reasoning categories. Comparing Graph-, Text-, and Hybrid-retrieval augmented generation methods, we find that Text-based achieves the highest overall accuracy, but that Graph-based provides the only correct answer for a subset of queries, particularly where the corpus contains competing values for the same fact. A failure analysis traces the majority of Graph-based retrieval errors to context dilution at high-degree hub nodes, an algorithmic limitation in graph traversal. We discuss implications for adaptive retrieval strategies that route queries to the appropriate modality.