A knowledge-graph-guided interactive system with controllable generation for enhancing the interpretation of Song Dynasty ceramics
Source abstract
Abstract
Abstract Ancient Chinese artifacts embody rich cultural knowledge and aesthetic value, yet such information is often fragmented and expressed through specialized terminology, creating barriers for public understanding. This study proposes an interactive museum experience system that integrates knowledge graphs with generative artificial intelligence to support semantic-driven interaction with Song Dynasty ceramics. A knowledge graph was constructed to structure information on ceramic types, patterns, and decorative areas, providing semantic guidance for generation and interaction process. A two-stage workflow combining Stable Diffusion with LoRA fine-tuning and the control capability of FLUX.1 Kontext was implemented to enable controllable ceramic image generation. Based on this framework, an interactive process allows users to explore ceramic features and generate images while learning contextual knowledge. Experimental results and user studies suggest that the system produces stylistically coherent ceramic images and shows potential for enhancing visitor engagement and cultural understanding.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Processing and 3D Reconstruction · Generative Adversarial Networks and Image Synthesis · Aesthetic Perception and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
Show source-provided references
1. Chen, Y., Yu, J. & Zhang, H. Strategies for promoting cultural heritage tourism based on sustainable development goals: a case study of ceramic culture. Sustainability 16, 1540 (2024).10.3390/su16041540 ↗
2. Li, H., Guo, L. & Chen, S. Research on digital protection and inheritance of pottery-making techniques based on virtual simulation technology. J. Phys. Conf. Ser. 1575, 012169 (2020).10.1088/1742-6596/1676/1/012169 ↗
3. Zou, L., Zhang, H. & Wu, W. Digital technology–enabled cultural heritage activation: strategies for sustainable cultural innovation. Sustainability 15, 1194 (2023).10.3390/su15021194 ↗
4. Ma, J., Jin, Y., Zhang, G. & Guo, H. Semantic-driven compositional network for fine-grained text-to-image generation. Soft Comput. 28, 13801–13816 (2024).
5. Wu, S. Study on the heritage and development of the Forbidden City’s ceramic culture through the lens of intercultural communication. Int. J. Educ. Humanit. 17, 349–354 (2024).10.54097/ejwy2h50 ↗
6. Zhang, Z. & Pan, W. The beauty of clay: exploring contemporary ceramic art as an aesthetic medium in education. Comunicar 32, 6 (2024).
7. Hansson, P. & Öhman, J. Museum education and sustainable development: a public pedagogy. Eur. Educ. Res. J. 21, 469–483 (2022).10.1177/14749041211056443 ↗
8. Bell, D. R. Aesthetic encounters and learning in the museum. Educ. Philos. Theory 49, 776–787 (2017).10.1080/00131857.2016.1214899 ↗
9. Chunlan, Y., Tengku Wook, T. S. M. & Rosdi, F. Advancing cultural heritage: a decadal review of digital transformation in Chinese museums. npj Herit. Sci. 13, 189 (2025).10.1038/s40494-025-01714-x ↗
10. Li, J., Zheng, X., Watanabe, I. & Ochiai, Y. A systematic review of digital transformation technologies in museum exhibition. Comput. Hum. Behav. 161, 108407 (2024).10.1016/j.chb.2024.108407 ↗
11. Orphanidou, Y., Efthymiou, L. & Panayiotou, G. Cultural heritage for sustainable education amidst digitalisation. Sustainability 16, 1540 (2024).10.3390/su16041540 ↗
12. Qian, J., Cheng, J., Zeng, Y. & Dian, W. T. Design and implementation of museum educational content based on mobile augmented reality. Comput. Syst. Sci. Eng. 36, 157–173 (2021).10.32604/csse.2021.014258 ↗
13. Liu, W. Exploration of ceramic culture inheritance and innovation paths for the digital era. Appl. Math. Nonlinear Sci. 9, 1 (2024).
14. Booth, P., Navarrete, T. & Ogundipe, A. Museum open data ecosystems: a comparative study. J. Doc. 78, 761–779 (2022).10.1108/JD-05-2021-0102 ↗
15. Zhang, J., Bai, S., Zeng, X., Liu, K. & Yuan, H. Supporting historic mural image inpainting by using coordinate attention aggregated transformations with U-Net-based discriminator. npj Herit. Sci. 13, 305 (2025).10.1038/s40494-025-01891-9 ↗
16. Tian, F., Wu, S., Zhang, X., Li, H. & Huang, Y. An empirical study of virtual museum based on dual-mode mixed visualization: the Sanxingdui Bronzes. npj Herit. Sci. 12, 146 (2024).10.1186/s40494-024-01241-1 ↗
17. Gong, Q., Wang, Y., Zhang, M. & Liu, J. User experience model and design strategies for virtual reality-based cultural heritage exhibition. Virtual Real. 28, 69 (2024).10.1007/s10055-024-00942-z ↗
18. Li, Y., Yang, R., Zou, J., Xu, H. & Tian, F. Human-centric virtual museum: redefining the museum experience through immersive and interactive environments. Int. J. Hum. Comput. Interact. 41, 8426–8437 (2024).10.1080/10447318.2024.2408861 ↗
19. Li, J. et al. Design and application of holography in mobile museums. Proc. SPIE 13509, 135091D (2025).
20. Wang, J. & Zakaria, S. A. Design application and evolution of 3D visualization technology in architectural heritage conservation: a citespace-based knowledge mapping and systematic review (2005–2024). Buildings 15, 1854 (2025).10.3390/buildings15111854 ↗
21. Dou, Z., Zhu, Q. & Li, Q. Twenty-four solar terms knowledge graph construction and intelligent question answering. J. Inf. Sci. 45, 154–170 (2019).
22. Bai, B. & Hou, W. The application of knowledge graphs in the Chinese cultural field: the ancient capital culture of Beijing. Herit. Sci. 11, 77 (2023).10.1186/s40494-023-00922-7 ↗
23. Yang, S. & Hou, M. Knowledge graph representation method for semantic 3d modeling of Chinese grottoes. Herit. Sci. 11, 266 (2023).10.1186/s40494-023-01084-2 ↗
24. Yuan, H., Zhao, L., Chen, Y. & Wang, P. Knowledge graph-based intelligent question answering system for ancient Chinese costume heritage. npj Herit. Sci. 13, 198 (2025).10.1038/s40494-025-01776-x ↗
25. Zhang, G. et al. CD-GAN: commonsense-driven generative adversarial network with hierarchical refinement for text-to-image synthesis. Intell. Comput. 2, 0017 (2023).10.34133/icomputing.0017 ↗
26. Meng, K. et al. Database construction and remodeling method on traditional Yi nationality patterns of China with GAN model. npj Herit. Sci. 13, 181 (2025).10.1038/s40494-025-01707-w ↗
27. Chen, Y. & Ma, J. An intelligent generative method of fashion design combining attribute knowledge and Stable Diffusion model. Text. Res. J. 95, 1231–1254 (2024).10.1177/00405175241289578 ↗
28. Bikakis, A., Hyvönen, E., Jean, S., Markhoff, B. & Mosca, A. Editorial: Special issue on semantic web for cultural heritage. Semant. Web 1, 1–5 (2020).
29. Huang, Y. Y., Yu, S. S., Chu, J. J., Fan, H. H. & Du, B. B. Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management. Herit. Sci. 11, 204 (2023).10.1186/s40494-023-01042-y ↗
30. Zhou, Y., Liu, Y., Shao, Y. & Chen, J. Fine-tuning diffusion model to generate new kite designs for the revitalization and innovation of intangible cultural heritage. Sci. Rep. 15, 7519 (2025).10.1038/s41598-025-92225-z ↗
31. Li, Y., Zhang, C., Li, Y., Sui, D. & Guo, M. An improved mural image restoration method based on diffusion model. npj Herit. Sci. 13, 347 (2025).10.1038/s40494-025-01914-5 ↗
32. Hu, Q. et al. Adaptive stylized image generation for traditional Miao Batik using style-conditioned LCM-LoRA-enhanced diffusion models. Mathematics 13, 1947 (2025).10.3390/math13121947 ↗
33. Dai, M., Feng, Y., Wang, R. & Jung, J. Enhancing the digital inheritance and development of Chinese intangible cultural heritage paper-cutting through Stable Diffusion LoRA models. Appl. Sci. 14, 11032 (2024).10.3390/app142311032 ↗
34. Xu, Z. et al. MuralDiff: diffusion for ancient murals restoration on large-scale pre-training. IEEE Trans. Emerg. Top. Comput. Intell. 8, 2169–2181 (2024).10.1109/TETCI.2024.3359038 ↗
35. Zhang, T., Wang, Z., Huang, J., Tasnim, M. M. & Shi, W. A survey of diffusion based image generation models: issues and their solutions. Preprint at https://doi.org/10.48550/arXiv.2308.13142 (2023).10.48550/arXiv.2308.13142 ↗
36. Cui, X., Feng, X. & Sun, S. Learning to generate urban design images from the conditional latent diffusion model. IEEE Access 12, 89135–89143 (2024).10.1109/ACCESS.2024.3419159 ↗
37. Videau, M., Knizev, N., Leite, A., Schoenauer, M. & Teytaud, O. Interactive latent diffusion model. In Proc. Genetic and Evolutionary Computation Conference, 586–596 (ACM, 2023).10.1145/3583131.3590471 ↗
38. Gerritsen, A. Ceramics manufacture in Song–yuan–ming Jingdezhen. J. Econ. Soc. Hist. Orient 52, 117–142 (2009).10.1163/156852009X405366 ↗
39. Sun, J. Identification of porcelain ewers in Tang, Song, and Yuan Dynasties by digital shape characterization. Ceram. Int. 49, 14246–14254 (2023).10.1016/j.ceramint.2023.01.011 ↗
40. Ma, D. Study on Cizhou-style inlaid porcelain, the Northern Song Dynasty (960–1127 AD), China. Archaeometry 63, 1178–1191 (2021).10.1111/arcm.12679 ↗
41. Tong, S. Illustrated Classification of Chinese Ceramics (Chemical Industry Press, 2008).
42. Qiu, D. Appreciation of Song and Yuan Dynasty Ceramics in China (Hunan Fine Arts Publishing House, 2006).
43. Shen, C. General Knowledge of Chinese Cultural Relics (Beijing Institute of Technology Press, 2017).
44. Dou, J., Qin, J., Jin, Z. & Li, Z. Knowledge graph based on domain ontology and natural language processing technology for Chinese intangible cultural heritage. J. Vis. Lang. Comput. 48, 19–28 (2018).10.1016/j.jvlc.2018.06.005 ↗
45. Lin, J., Zhao, Y., Huang, W. et al. Domain knowledge graph-based research progress of knowledge representation. Neural Comput. Appl. 33, 681–690 (2021).10.1007/s00521-020-05057-5 ↗
46. Cui, Y. et al. Linking past insights with contemporary understanding: an ontological and knowledge graph approach to the transmission of ancient Chinese classics. Herit. Sci. 12, 382 (2024).10.1186/s40494-024-01504-x ↗
47. Luo, S. & Dong, Y. Classifying cultural artifact knowledge for creative design. J. Zhejiang Univ. (Eng. Sci.) 51, 89–95 (2017).
48. Peng, C. et al. Knowledge graphs: opportunities and challenges. Artif. Intell. Rev. 56, 13071–13102 (2023).10.1007/s10462-023-10465-9 ↗
49. Vukotic, A., Watt, N., Abedrabbo, T., Fox, D. & Partner, J. Neo4j in Action (Manning Publications, 2014).
50. Black Forest Labs et al. FLUX.1 Kontext: flow matching for in-context image generation and editing in latent space. Preprint at https://doi.org/10.48550/arXiv.2506.15742 (2025).10.48550/arXiv.2506.15742 ↗
51. O’Brien, H. L., Cairns, P. & Hall, M. A practical approach to measuring user engagement with the refined User Engagement Scale (UES) and new UES short form. Int. J. Hum. Comput. Stud. 112, 28–39 (2018).10.1016/j.ijhcs.2018.01.004 ↗
52. Csikszentmihalyi, M. Flow: The Psychology of Optimal Experience (Harper Collins, 2009).
53. Falk, J. H. & Dierking, L. D. The Museum Experience Revisited (Routledge, 2013).