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<p>Dongba script, the world’s only living pictographic writing system, lacks datasets organized at the ritual event level and addressing expert–novice cognitive asymmetry. We present RACK-Dongba, a four-layer neuro-symbolic framework integrating: (1) AHP-based user needs analysis revealing complete non-overlap between expert (<i>n</i> = 7) and novice (<i>n</i> = 18) top-3 priorities; (2) NS-DPOnto ontology extending CIDOC CRM with a RitualEvent semantic layer; (3) CLIP-ViT-B/32 cross-document matching and GLM-4V multimodal annotation; and (4) a SWRL-based cognitive load classifier with dual-layer Neo4j query templates. The DPKG dataset comprises 1788 character entities, 3 ritual event nodes (481 ritual-affiliated), and cognitive load labels (HIGH: 161, MEDIUM: 1081, LOW: 546). Experiment 1 achieves F1 = 77.7%, outperforming CIDOC-KG (44.0%) and BERT-KG (15.8%). Experiment 2 (<i>n</i> = 32, NASA-TLX) validates classification efficacy (<i>F</i>(2,62) = 363.298, <i>p</i> &lt; 0.001, partial <i>η</i>² = 0.921). RACK-Dongba is the first semantic knowledge graph for the Dongba script and the first pictographic heritage system to operationalize cognitive needs as a design principle with dual-layer audience-adaptive access.</p>

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A ritual-aware cognitive-adaptive knowledge graph for the Dongba pictographic script heritage

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  • 发表日期：

    2026年5月13日

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  • Bize Wei,
  • Yu Qiao

摘要

Dongba script, the world’s only living pictographic writing system, lacks datasets organized at the ritual event level and addressing expert–novice cognitive asymmetry. We present RACK-Dongba, a four-layer neuro-symbolic framework integrating: (1) AHP-based user needs analysis revealing complete non-overlap between expert (n = 7) and novice (n = 18) top-3 priorities; (2) NS-DPOnto ontology extending CIDOC CRM with a RitualEvent semantic layer; (3) CLIP-ViT-B/32 cross-document matching and GLM-4V multimodal annotation; and (4) a SWRL-based cognitive load classifier with dual-layer Neo4j query templates. The DPKG dataset comprises 1788 character entities, 3 ritual event nodes (481 ritual-affiliated), and cognitive load labels (HIGH: 161, MEDIUM: 1081, LOW: 546). Experiment 1 achieves F1 = 77.7%, outperforming CIDOC-KG (44.0%) and BERT-KG (15.8%). Experiment 2 (n = 32, NASA-TLX) validates classification efficacy (F(2,62) = 363.298, p < 0.001, partial η² = 0.921). RACK-Dongba is the first semantic knowledge graph for the Dongba script and the first pictographic heritage system to operationalize cognitive needs as a design principle with dual-layer audience-adaptive access.