{
  "abstract": "Health assessment scales are essential to measurement-based care, yet their adoption is hindered by difficulties in accessing relevant knowledge dispersed across unstructured scientific literature. This study explores a knowledge graph (KG) approach for the structured representation and semantic integration of health assessment scales and their associated literature metadata, aiming to propose a simple yet effective framework for this task. We defined a fine-grained KG schema that incorporates scale component entities and their relations, cross-scale mappings, and contextual associations. A construction pipeline was developed that combines prompt-based extraction of scale components and relations, type-specific entity normalization and relation fusion, cross-scale semantic mapping, and contextual linkage. To evaluate the pipeline, we built a prototype KG from 55 scales and 234 scientific articles, supported by a manually annotated dataset for cross-sentence relation extraction. Experiments with GLM-4-plus achieved F1 scores of 64.90% and 65.81% for component entity and relation extraction, respectively. Normalization yielded ARI and FMI scores above 0.54 for AssessmentScales and above 0.63 for MeasurementConcepts. Cross-scale mapping achieved F1 scores of 0.80 for AssessmentScales, 0.77 for MeasurementConcepts, and 0.85 for Items. The resulting prototype KG contains 6,468 concept-level entities and 32,096 triples, accessible through a public web interface. The scale-specific KG framework represents a practical method for structuring scale-related knowledge under low-annotation conditions, broadening medical KG research and supporting the advancement of measurement-based care.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s41666-026-00247-7.",
  "abstract_shown": true,
  "affiliations": [
    "Beijing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Haixia Sun"
    },
    {
      "affs": [
        0
      ],
      "name": "Liang Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhenli Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiao Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Junlian Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Jie Hao"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Qing Qian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/70wm27yxdg9r9.bib",
  "cited_by": 0,
  "doi": "10.1007/s41666-026-00247-7",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719916/?tool=EBI",
  "id": "70wm27yxdg9r9",
  "issns": [
    "2509-4971",
    "2509-498X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13719916",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "1312|Biomedical Text Mining and Ontologies"
    ]
  ],
  "openalex": "W7171615905",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719916/pdf/?tool=EBI",
  "pmcid": "PMC13719916",
  "pmid": "42964557",
  "published": "2026-07-29",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/70wm27yxdg9r9.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:45Z",
      "native_id": "PMC:PMC13719916",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719916"
    },
    {
      "first_seen": "2026-11-02T12:23:47Z",
      "native_id": "PMC13719916",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s41666-026-00247-7"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Biomedical Text Mining and Ontologies",
      "subfield": "molecular-biology"
    }
  ],
  "title": "A Knowledge Graph Framework for Linking Health Assessment Scales and Scientific Literature in Low-Annotation Settings: Development and Evaluation Study",
  "title_text": "A Knowledge Graph Framework for Linking Health Assessment Scales and Scientific Literature in Low-Annotation Settings: Development and Evaluation Study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:53Z",
  "url": "https://research.draconitic.com/paper/70wm27yxdg9r9",
  "venue": "Journal of healthcare informatics research",
  "versions": []
}