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图学学报 ›› 2026, Vol. 47 ›› Issue (4): 904-914.DOI: 10.11996/JG.j.2095-302X.2026040904

• 工业设计 • 上一篇    下一篇

知识图谱驱动下的陕西非遗面花交互地图设计研究

白晓波(), 余雅情, 钦松   

  1. 西安理工大学艺术与设计学院陕西 西安 710054
  • 收稿日期:2026-01-22 接受日期:2026-04-29 出版日期:2026-08-31 发布日期:2026-08-31
  • 通讯作者:白晓波,E-mail:xiaobo413@126.com
  • 基金资助:
    陕西省社会科学基金(2022J032)

Research on the interactive map design of Shaanxi intangible cultural heritage dough figurines driven by knowledge graph

BAI Xiaobo(), YU Yaqing, QIN Song   

  1. College of Art and Design, Xi’an University of Technology, Xi’an Shaanxi 710054, China
  • Received:2026-01-22 Accepted:2026-04-29 Published:2026-08-31 Online:2026-08-31
  • Contact: BAI Xiaobo,E-mail:xiaobo413@126.com
  • Supported by:
    Shaanxi Provincial Social Science Fund(2022J032)

摘要:

针对非遗传承中认知成本高、知识体系零散和文化信息易流失等现实难题,以陕西面花为典型案例,提出一种融合知识图谱的交互地图设计方法。首先,构建包含传承人、作品、技法、仪式场合、视觉符号、社会意义与地理信息等在内的13类实体关系的领域本体,并融入空间属性规范以支持地理检索与空间分析;其次,提出Aho-Corasick词典匹配规则模板与领域自适应BERT相结合的混合信息抽取框架,在小样本语料上实现实体与关系的联合抽取与置信度融合,实体识别与关系抽取F1值分别达到0.88与0.84,性能优于单一规则或单一深度模型;随后,建立融合空间索引的陕西面花知识图谱,依托Neo4j完成三元组存储、实体链接与关联查询;最后,开发知识图谱与地理信息深度融合的交互式导览地图系统,实现传承人分布、作品溯源与技法传承等多维度查询与空间分析,并支持图谱数据增删改的动态维护。该研究构建了非遗知识抽取、建模、存储及可视化的端到端解决方案,有效提升了非遗知识组织精度与空间化传播体验,为同类非遗项目提供了可复用的实践范式。

关键词: 非遗面花, 交互地图, 知识图谱, 语义化, 信息抽取

Abstract:

To address practical challenges in Intangible Cultural Heritage (ICH) transmission, such as high cognitive costs, fragmented knowledge systems, and the easy loss of cultural information, a knowledge graph-driven interactive map design method was proposed using Shaanxi dough figurines as a representative case. First, a domain ontology was constructed that covered thirteen types of entity relationships, including inheritors, artworks, techniques, ritual occasions, visual symbols, social meanings, and geographical information, and spatial attribute specifications were incorporated to support geographic retrieval and spatial analysis. Second, a hybrid information extraction framework combining Aho-Corasick dictionary-matching rule templates with a domain-adaptive BERT model was proposed. This framework enabled joint extraction of entities and relations with confidence fusion on small-sample corpora, achieving F1 scores of 0.88 for entity recognition and 0.84 for relation extraction, outperforming both single-rule and single deep-learning models. Subsequently, a knowledge graph of Shaanxi dough figurines integrated with spatial indexes was established, utilizing Neo4j for triplet storage, entity linking, and associative querying. Finally, an interactive navigation map system featuring deep integration of the knowledge graph with geographic information was developed. This system enabled multi-dimensional querying and spatial analysis, including the inheritor distribution, artwork provenance, and technique transmission, while supporting dynamic maintenance of the graph data through addition, deletion, and modification operations. This study constructed an end-to-end solution encompassing knowledge extraction, modeling, storage, and visualization for ICH, effectively enhancing the precision of knowledge organization and the experience of spatial dissemination, thereby providing a reusable practical paradigm for similar ICH projects.

Key words: intangible cultural heritage dough figurines, interactive map, knowledge graph, semantic modeling, information extraction

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