@inproceedings{davidsson-harmouch-2026-end,
    title = "End-to-End Graph Retrieval Pipeline for Specialized Domains",
    author = "Davidsson, Haraldur  and
      Harmouch, Hazar",
    editor = "S{\'e}rasset, Gilles  and
      Gkirtzou, Katerina  and
      Cochez, Michael  and
      Kalo, Jan-Christoph",
    booktitle = "Proceedings of the Knowledge Graphs and Large Language Models Workshop ({KG}-{LLM}) @ {LREC}26",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.kallm-1.16/",
    doi = "10.63317/3xjhui2zyiws",
    pages = "155--165",
    abstract = "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."
}