TY - JOUR
KW - Artificial intelligence
KW - Computation theory
KW - Computational framework
KW - Cultural heritage preservation
KW - Digital humanities
KW - Digital presentation
KW - E-learning
KW - Embeddedness
KW - Historic preservation
KW - Intangible cultural heritages
KW - Interactive learning
KW - Iterative methods
KW - Neural-symbolic architecture
KW - Oral tradition
KW - Semiotic alignment
KW - Semiotics
AU - Liuxun Zhang
AU - Zhouluo Wang
AU - Rulan Yang
AU - Qiang Yi
AB - The preservation of intangible cultural heritage (ICH) faces significant and multifaceted challenges due to its ephemeral nature, reliance on oral traditions, and contextual embeddedness within lived cultural experiences. Traditional preservation approaches - such as textual documentation, static archiving, and audiovisual recordings - often fall short in capturing the dynamic, embodied, and performative characteristics that define ICH practices. To overcome these limitations, we propose an innovative computational framework that integrates advanced neural representations with structured symbolic logic and contextual grounding mechanisms. We introduce a novel neural-symbolic architecture capable of modeling the fluid, multimodal, and socially constructed nature of intangible cultural knowledge. Our approach includes a culturally informed reasoning strategy that enables the system to align observed cultural signals with both canonical forms and evolving variants within a specific tradition. This is further enhanced by a self-supervised semiotic alignment module, which dynamically adapts through iterative engagement with context-specific cues and emergent performative deviations. By leveraging cutting-edge artificial intelligence, our framework enables the digital preservation, interactive representation, and inclusive transmission of ICH, ensuring its resilience, relevance, and accessibility across generations and communities in a rapidly evolving global landscape.
DO - 10.1109/ACCESS.2025.3588520
N1 - Type: Article
N2 - The preservation of intangible cultural heritage (ICH) faces significant and multifaceted challenges due to its ephemeral nature, reliance on oral traditions, and contextual embeddedness within lived cultural experiences. Traditional preservation approaches - such as textual documentation, static archiving, and audiovisual recordings - often fall short in capturing the dynamic, embodied, and performative characteristics that define ICH practices. To overcome these limitations, we propose an innovative computational framework that integrates advanced neural representations with structured symbolic logic and contextual grounding mechanisms. We introduce a novel neural-symbolic architecture capable of modeling the fluid, multimodal, and socially constructed nature of intangible cultural knowledge. Our approach includes a culturally informed reasoning strategy that enables the system to align observed cultural signals with both canonical forms and evolving variants within a specific tradition. This is further enhanced by a self-supervised semiotic alignment module, which dynamically adapts through iterative engagement with context-specific cues and emergent performative deviations. By leveraging cutting-edge artificial intelligence, our framework enables the digital preservation, interactive representation, and inclusive transmission of ICH, ensuring its resilience, relevance, and accessibility across generations and communities in a rapidly evolving global landscape.
SP - 126245
EP - 126260
TI - Digital Presentation and Interactive Learning for Intangible Cultural Heritage Preservation Using Artificial Intelligence
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011150368&doi=10.1109%2FACCESS.2025.3588520&partnerID=40&md5=f24a8e4ad3ac0e4fd8027f442717c2c9
VL - 13
ER -