{
  "abstract": "Knowledge graph construction typically involves data preprocessing, entity recognition, relation extraction, knowledge fusion, conflict resolution, and quality assessment. However, heterogeneous documents in substation operation and maintenance (O&M), including technical standards, defect records, work tickets, operational logs, and scanned or mixed-format documents, exhibit dense domain terminology, inconsistent expressions, cross-source conflicts, and incomplete provenance, which make accurate and traceable knowledge construction challenging. Existing large-language-model-based and pipeline-based methods have improved automatic knowledge extraction, but their processing stages are usually weakly coupled, making it difficult to reuse cross-stage evidence, trace downstream errors to responsible upstream tasks, and perform targeted rework. To address these limitations, this paper proposes a shared-task-state-driven multi-agent collaborative knowledge graph construction framework (MAC-CKG). The framework introduces a task orchestrator and a shared task-state pool to coordinate five agents for data processing, entity recognition, relation extraction, knowledge fusion, and quality assessment; provenance, confidence, conflict, and review information are preserved across stages, while ontology constraints, multifactor confidence, and the two-threshold quality-routing mechanism jointly support knowledge insertion, human review, and targeted rework. For evaluation, we constructed a heterogeneous substation O&M dataset comprising 96 heterogeneous source items (including documents and structured knowledge resources) and 17,220 standardized input units, yielding a provenance-verified candidate subset of 9519 entities and 9258 relations. An independently annotated 20-document end-to-end benchmark was separately used for raw-document extraction evaluation. Across three independent runs on this benchmark, MAC-CKG achieves mean entity, conditional relation, and strict triple F1 scores of 85.96%, 62.50%, and 62.31%, respectively, outperforming SAC-KG by 2.83, 5.77, and 6.34 percentage points and PiVe by 4.48, 8.32, and 8.94 percentage points. Independent-benchmark ablation further reduces triple F1 from 62.31% to 59.68% without task orchestration and to 57.41% without the shared task-state pool, showing sensitivity to these components under the evaluated configurations, with inference cost considered separately.",
  "abstract_shown": true,
  "affiliations": [
    "Guangdong Power Grid Co., Ltd., Guangzhou 510600, China",
    "School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Linqing Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenyong Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Shuo Yu"
    },
    {
      "affs": [
        1
      ],
      "name": "Chaoduan Deng"
    },
    {
      "affs": [
        1
      ],
      "name": "Bin Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dfa2grs0kpj0s.bib",
  "cited_by": 0,
  "doi": "10.3390/app162010394",
  "fulltext_url": "https://www.mdpi.com/2076-3417/16/20/10394",
  "id": "dfa2grs0kpj0s",
  "issns": [
    "2076-3417"
  ],
  "landing_url": "https://doi.org/10.3390/app162010394",
  "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",
      "QC1-999"
    ],
    [
      "lcc",
      "QD1-999"
    ],
    [
      "lcc",
      "QH301-705.5"
    ],
    [
      "lcc",
      "T"
    ],
    [
      "lcc",
      "TA1-2040"
    ],
    [
      "openalex",
      "1702|Topic Modeling"
    ]
  ],
  "openalex": "W7223057226",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/dfa2grs0kpj0s.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:21:21Z",
      "native_id": "060e8c2083da4868b8fa0220d2ca17fe",
      "source": "doaj",
      "url": "https://doi.org/10.3390/app162010394"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "lcc:QH301-705.5",
      "subfield": "general"
    },
    {
      "field": "chemistry",
      "origin": "lcc:QD1-999",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "lcc:QC1-999",
      "subfield": "general"
    }
  ],
  "title": "A Shared-Task-State-Driven Multi-Agent Framework for Knowledge Graph Construction in Substation Operation and Maintenance",
  "title_text": "A Shared-Task-State-Driven Multi-Agent Framework for Knowledge Graph Construction in Substation Operation and Maintenance",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:21Z",
  "url": "https://research.draconitic.com/paper/dfa2grs0kpj0s",
  "venue": "Applied Sciences",
  "versions": []
}