@inproceedings{ella-etal-2026-disambiguating,
    title = "Disambiguating Geographic Names in Biodiversity Occurrence Data: A Retrieval-Augmented Generation Approach",
    author = "Ella, Yanni Jose C.  and
      Laviste, Monica Ashley R.  and
      Lastimoso, John Michael L.  and
      Santia{\~n}ez, Wilfred John E.  and
      Batista-Navarro, Riza  and
      Gabud, Roselyn Santos",
    editor = "Grasso, Francesca  and
      Basile, Valerio  and
      Bosco, Cristina  and
      Ibrohim, Muhammad Okky  and
      Skeppstedt, Maria  and
      Stede, Manfred",
    booktitle = "Proceedings of the 2nd Workshop on Ecology, Environment, and Natural Language Processing",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.nlp4ecology-1.4/",
    doi = "10.63317/3m7qkiiy984w",
    pages = "42--52",
    abstract = "The availability of georeferenced coordinates is essential for biodiversity research, as it enables species distribution modeling and supports conservation planning. However, datasets often contain ambiguous or inconsistent geographic names that reduce spatial accuracy and underscore the need for methods that resolve geographic name ambiguity. While traditional named entity linking strategies are well established, they remain limited in low-resource domains, e.g., in biodiversity contexts, due to the scarcity of annotated training data and high lexical ambiguity of local geographic names. This study proposes a Retrieval-Augmented Generation (RAG) framework to automatically disambiguate Philippine seaweed-related geographic names in databases and literature. This approach utilizes a custom knowledge base of gazetteers to support large language models (LLMs) in the task of geospatial disambiguation. With a disambiguation accuracy of 87.8{\%} within a 5 km distance error threshold, our evaluation shows that the RAG-enabled pipeline significantly outperforms standard LLM baselines (Accuracy@5km = 0{\%}), demonstrating the need for external knowledge to resolve geospatial ambiguity."
}