@inproceedings{cortes-2026-comparing,
    title = "Comparing {LLM}-Based Knowledge Graph Extraction Approaches on Literary Studies in {S}panish: A Case Study on Orbis Tertius",
    author = "Cortes, Federico",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.nslp-1.8/",
    doi = "10.63317/5kjis3dy8i7k",
    pages = "78--87",
    abstract = "Knowledge graph construction from scholarly text increasingly relies on large language models, yet different extraction architectures produce different graphs. Literary studies poses particular challenges: meaning is interpretive rather than factual, and the boundaries of relevant knowledge are determined by hermeneutic frameworks rather than empirical verification. We compare two LLM-based extraction frameworks{---}entity-anchored extraction (KGGen) and open extraction with schema canonicalization (EDC){---}on 472 Spanish-language literary studies articles from Orbis Tertius (1996{--}2024). Despite fundamental architectural differences, both methods converge on key findings: cultural framing dominates literary discourse by 2.2{--}2.5{\texttimes} over textual framing (p {\ensuremath{<}} .001), and core author networks remain consistent across approaches. The methods diverge in entity composition: KGGen captures more proper names (40.7{\%} vs. 18.7{\%}), while EDC captures more abstract concepts (42.8{\%}) and preserves Spanish predicates with 21,025 semantic definitions. Convergent findings across architecturally different methods merit higher confidence, and we identify methodological considerations for knowledge graph construction from humanities scholarship."
}