@inproceedings{pan-etal-2025-taxonomy,
    title = "Taxonomy-Driven Knowledge Graph Construction for Domain-Specific Scientific Applications",
    author = "Pan, Huitong  and
      Zhang, Qi  and
      Adamu, Mustapha  and
      Dragut, Eduard  and
      Latecki, Longin Jan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.223/",
    pages = "4295--4320",
    ISBN = "979-8-89176-256-5",
    abstract = "We present a taxonomy-driven framework for constructing domain-specific knowledge graphs (KGs) that integrates structured taxonomies, Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Although we focus on climate science to illustrate its effectiveness, our approach can potentially be adapted for other specialized domains. Existing methods often neglect curated taxonomies{---}hierarchies of verified entities and relationships{---}and LLMs frequently struggle to extract KGs in specialized domains. Our approach addresses these gaps by anchoring extraction to expert-curated taxonomies, aligning entities and relations with domain semantics, and validating LLM outputs using RAG against the domain taxonomy. Through a climate science case study using our annotated dataset of 25 publications (1,705 entity-publication links, 3,618 expert-validated relationships), we demonstrate that taxonomy-guided LLM prompting combined with RAG-based validation reduces hallucinations by 23.3{\%} while improving F1 scores by 13.9{\%} compared to baselines without the proposed techniques. Our contributions include: 1) a generalizable methodology for taxonomy-aligned KG construction; 2) a reproducible annotation pipeline, 3) the first benchmark dataset for climate science information retrieval; and 4) empirical insights into combining structured taxonomies with LLMs for specialized domains. The dataset, including expert annotations and taxonomy-aligned outputs, is publicly available at \url{https://github.com/Jo-Pan/ClimateIE}, and the accompanying framework can be accessed at \url{https://github.com/Jo-Pan/TaxoDrivenKG}."
}