{
  "abstract": "Emergency management requires structured representations that can integrate heterogeneous event reports and support cross-event analysis. This study develops an emergency event knowledge graph (EE-KG) by combining fine-grained event classification, lifecycle-oriented ontology modeling, and ontology-guided knowledge extraction. We collected 6,418 Chinese emergency-related reports from public government portals, news websites, and safety information platforms published between 2000 and 2024, and aggregated them into 1,971 event records. A manually annotated multi-label dataset was constructed according to four primary categories and 36 subcategories. Four classification strategies were evaluated: fine-tuned ERNIE 3.0, prompt-based GLM-4-9B-Chat, agent-based GLM-4-9B-Chat with reflective reasoning, and fine-tuned GLM-4-9B-Chat. The fine-tuned GLM-4-9B-Chat achieved the best performance, with an accuracy of 0.80 and a micro-F1 score of 0.85. Classification outputs were used to assign standardized event types to event nodes, while a five-dimensional ontology guided the extraction of event description, impact assessment, emergency response, recovery and reconstruction, and evaluation information. The resulting EE-KG contains 16,057 nodes and 18,428 relationships. Expert review indicates that the proposed framework provides a reusable structure for emergency event knowledge organization and analysis.",
  "abstract_shown": true,
  "affiliations": [
    "Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences",
    "National Science Library, Chinese Academy of Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Heng Yang"
    },
    {
      "affs": [
        1
      ],
      "name": "Guangzu Bai"
    },
    {
      "affs": [
        0
      ],
      "name": "Nan Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Sili Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0knh2kgef6kwg.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-57570-7",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-57570-7",
  "id": "0knh2kgef6kwg",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-57570-7",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1702|Topic Modeling"
    ]
  ],
  "openalex": "W7170053811",
  "pdf_url": "https://www.nature.com/articles/s41598-026-57570-7_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/0knh2kgef6kwg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:54Z",
      "native_id": "b8103201f0a44c92a6cf6e1f310576be",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-57570-7"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Development of knowledge graphs for emergency events utilizing pre-trained language models",
  "title_text": "Development of knowledge graphs for emergency events utilizing pre-trained language models",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:54Z",
  "url": "https://research.draconitic.com/paper/0knh2kgef6kwg",
  "venue": "Scientific Reports",
  "versions": []
}