Knowledge Graph-Based Multimodal Framework for Semantic Analysis and Design Assistance of Traditional Cultural...

Traditional cultural pattern analysis requires the integration of visual characteristics with symbolic meanings, regional contexts, and cultural knowledge to support semantic interpretation and design assistance. Although existing approaches have contributed to visual feature learning and multimodal analysis, integrating visual features, semantic knowledge, and cultural contextual information within a unified framework remains challenging. This study presents a knowledge graph-based multimodal framework for semantic understanding and knowledge-guided design assistance of traditional cultural patterns, including decorative motifs in textiles, embroidery, ceramics, paintings, and other cultural heritage artifacts. The framework jointly processes traditional cultural pattern images and associated descriptive metadata. EfficientNet-Lite, a lightweight Convolutional Neural Network (CNN), extracts visual features, while a Graph Neural Network (GNN) maps these representations to structured cultural knowledge for semantic reasoning. Multimodal fusion integrates visual, semantic, and cultural contextual representations, and Retrieval-Augmented Generation (RAG) with knowledge filtering supports semantic explanation and pattern design assistance. The framework integrates Motif-Sikka, OS-CTPIG, and CraftMotifDB and follows a workflow comprising preprocessing, visual feature extraction, semantic mapping, knowledge graph reasoning, multimodal fusion, retrieval, and design generation. The framework achieved classification accuracies of 97.82% for Motif-Sikka, 97.35% for OS-CTPIG, and 96.94% for CraftMotifDB. Generated outputs achieved a Bilingual Evaluation Understudy (BLEU) score of up to 0.89, Structural Similarity Index Measure (SSIM) of up to 0.94, Fréchet Inception Distance (FID) as low as 21.35, and Learned Perceptual Image Patch Similarity (LPIPS) as low as 0.15. These results demonstrate the framework's performance in classification and design-output evaluation while supporting knowledge-guided semantic reasoning and design assistance for traditional cultural patterns.