Smart Management and Dissemination System for Cultural Heritage Based on Knowledge Graphs and Digital Twins
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Abstract
Cross-modal information fusion and digital twin technologies have become essential for intelligent sensing, data integration, and real-time decision support in complex engineering systems. To address the semantic inconsistency between geometric models and heterogeneous knowledge representations, this study proposes a Digital Twin– Knowledge Graph (DT-KG) deep fusion framework based on cross-modal ontology mapping for smart management and digital dissemination. The framework combines joint entity–relationship extraction, 3D point cloud semantic segmentation, IoT sensing, and spatial–semantic dynamic alignment to establish bidirectional associations between physical objects and semantic knowledge. A contrastive learning-based alignment algorithm projects heterogeneous spatial and semantic features into a unified latent space, enabling accurate cross-modal mapping and intelligent reasoning. Based on the integrated architecture, an intelligent management and interaction platform is developed to support structural anomaly analysis, semantic retrieval, and immersive visualization. Experimental results demonstrate high mapping accuracy, efficient multimodal retrieval, and stable real-time rendering performance while maintaining robust operation under concurrent requests. Beyond cultural heritage applications, the proposed DT-KG framework provides an effective methodology for intelligent information fusion, digital twin communication, multimodal sensing, and adaptive spatial information processing, offering valuable engineering references for Electromagnetic Waves, Antennas and Propagation in areas such as wireless sensing, distributed perception, and smart infrastructure management.
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