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<p>The Yellow River Basin encompasses diverse regional cultures. A knowledge graph (KG) integrating spatial characteristics and semantic hierarchies enables the structured representation and organization of intangible cultural heritage (ICH). This study investigates ICH knowledge organization in the Yellow River Basin by integrating spatial analysis and KG construction. The nearest-neighbor index, geographic concentration, and kernel density estimation are used to assess spatial patterns and identify clusters of ICH resources. Extracted entities and relations are modeled into a KG using Neo4j, enabling semantic queries. By combining spatial analytical outcomes with semantic structures, this study builds a comprehensive KG of ICH. The findings reveal distinct spatial distribution patterns, characterized by significant spatial heterogeneity and a clear gradient pattern from low to medium to high density. This research provides methodological support and a systematic framework for regional ICH knowledge modeling and intelligent services, contributing to cultural ecological conservation and high-quality development strategies.</p>

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Knowledge graph for intangible cultural heritage in Yellow River Basin integrating spatial characteristics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月15日

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  • Xiaoyin Fang,
  • Muzhe Han

摘要

The Yellow River Basin encompasses diverse regional cultures. A knowledge graph (KG) integrating spatial characteristics and semantic hierarchies enables the structured representation and organization of intangible cultural heritage (ICH). This study investigates ICH knowledge organization in the Yellow River Basin by integrating spatial analysis and KG construction. The nearest-neighbor index, geographic concentration, and kernel density estimation are used to assess spatial patterns and identify clusters of ICH resources. Extracted entities and relations are modeled into a KG using Neo4j, enabling semantic queries. By combining spatial analytical outcomes with semantic structures, this study builds a comprehensive KG of ICH. The findings reveal distinct spatial distribution patterns, characterized by significant spatial heterogeneity and a clear gradient pattern from low to medium to high density. This research provides methodological support and a systematic framework for regional ICH knowledge modeling and intelligent services, contributing to cultural ecological conservation and high-quality development strategies.