Construction of Chinese Traditional Culture Knowledge Graph and Intelligent Question Answering System Based on Graphormer
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Abstract
Traditional graph neural networks have limitations in modeling long-range multi-hop relation paths and sparse semantic entity representations when constructing knowledge graphs for Chinese traditional culture. To address variant character forms, syntactic abbreviations, and sparse entity semantics in ancient texts, this paper proposes a knowledge graph construction and question-answering system based on Graphormer. The Confucian four-element inheritance structure of “teacher-student-writing-school-event” and the three-element mapping of “rites, textiles, and crafts” are formalized as node types and directed relations in a knowledge graph. An edge-type encoding and path-constraint mechanism is constructed accordingly. Based on this structure, Graphormer’s shortest-path distance bias and edge-feature encoding are guided to prioritize multi-hop paths consistent with Confucian transmission logic. By embedding the philosophical transmission structure into a graph attention mechanism, the method achieves collaborative modeling of sparse semantic relations and long-range dependencies. Experiments show that the proposed method achieves Hits@10 of 0.659 in the knowledge completion task and accuracies of 0.773, 0.715, and 0.667 in four-hop multi-hop question answering, verifying the effectiveness of the structural expression and reasoning paradigm.
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References
D. Sun, Y. Zheng, and C. Huo, “A Study on the Dissemination Paths of China’s Excellent Traditional Culture in the Context of Intelligence,” Chinese Traditional Culture, vol. 12, no. 3, pp. 474-479, 2024, doi: 10.12677/cnc.2024.123075.
J. Yang and M. Zhu, “An Analysis of the Paths of Artificial Intelligence Empowering the Education of China’s Ex-cellent Traditional Culture,” College and Job, vol. 14, no. 4, pp. 45-51, 2025, doi: 10.12677/ve.2025.144152.
Y. Cui, J. Shen, J. Wu, and M. Lv, “Linking past insights with contemporary understanding: An ontological and knowledge graph approach to the transmission of ancient Chinese classics,” Heritage Science, vol. 12, no. 1, pp. 382-399, 2024, doi: 10.1186/s40494-024-01504-x.
B. Bai and W. Hou, “The application of knowledge graphs in the Chinese cultural field: The ancient capital culture of Beijing,” Heritage Science, vol. 11, no. 1, pp. 77-93, 2023, doi: 10.1186/s40494-023-00922-7.
L. Wang, F. N. Mohamed, and Z. Liu, “Lanjiefu in Wenzhou City under the Perspective of Digital Humanities: Constructing a Knowledge Graph to Empower the Cultural Resource Base,” International Journal of Education and Humanities, vol. 5, no. 3, pp. 404-414, 2025, doi: 10.58557/(ijeh).v5i3.322.
Z. Qian, T. Chen, Y. Xu, Q. Li, and D. Zhang, “Research on the Construction and Application of Knowledge Graphs for Ancient Classical Dictionaries,” Library Journal, vol. 42, no. 388, pp. 82-88, 2023, [Online]. Available: https://www.libraryjournal.com.cn/CN/.
L. Liu, Y. Qi, C. Liu, W. Li, and D. Wang, “Research on Ancient Book Citations and the Construction of a Full-Text Knowledge Base Based on Computational Humanities,” Journal of the China Society for Scientific and Technical Information, vol. 42, no. 12, pp. 1498-1512, 2023.
Liu, S, H. Yang, J. Li, and S. Kolmanic, “Preliminary study on the knowledge graph construction of Chinese ancient history and culture,” Information, vol. 11, no. 4, pp. 186-206, 2020, doi: 10.3390/info11040186.
W. Chansanam, Y. Jaroenruen, N. Kaewboonma, and K. Tuamsuk, “Culture knowledge graph construction techniques,” Education for Information, vol. 38, no. 3, pp. 233-264, 2022, doi: 10.3233/EFI-220028.
L. Xu, L. Lu, and M. Liu, “Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources,” Heritage Science, vol. 11, no. 1, pp. 222-238, 2023, doi: 10.1186/s40494-023-01068-2.
H. Yuan, Y. Li, B. Wang, K. Liu, and J. Zhang, “Knowledge graph-based intelligent question answering system for ancient Chinese costume heritage,” Npj Heritage Science, vol. 13, no. 1, pp. 198-213, 2025, doi: 10.1038/s40494-025-01776-x.
M. Zhang, G. Geng, S. Zeng, and H. Jia, “Knowledge graph completion for the Chinese text of cultural relics based on bidirectional encoder representations from transformers with entity-type information,” Entropy, vol. 22, no. 10, pp. 1168-1182, 2020, doi: 10.3390/e22101168.
J. Liu, X. Ma, L. Wang, and L. Pei, “How Can Generative Artificial Intelligence Techniques Facilitate Intelligent Research into Ancient Books?,” ACM Journal on Computing and Cultural Heritage, vol. 17, no. 4, pp. 1-20, 2024, doi: 10.1145/3690391.
Q. Wu, W. Zhao, C. Yang, H. Zhang, F. Nie, H. Jiang, et al., “Sgformer: Simplifying and empowering transformers for large-graph representations,” Advances in Neural Information Processing Systems, vol. 36, no. 2826, pp. 64753-64773, 2023, doi: 10.48550/arXiv.2306.10759.
C. Zhou, Q. Yu, L. Zhang, Z. Hu, and M. Zhao, “Overview of Research Progress in Graph Transformers,” Journal of Computer Engineering & Applications, vol. 60, no. 14, pp. 1-37, 2024, doi: 10.3778/j.issn.1002-8331.2308-0216.
Y. Wang, J. Liu, W. Wang, J. Chen, X. Yang, L. Sang, et al., “Construction of cultural heritage knowledge graph based on graph attention neural network,” Applied Sciences, vol. 14, no. 18, pp. 8231-8258, 2024, doi: 10.3390/app14188231.
W. Hou, B. Bai, and C. Cai, “CR-TransR: A knowledge graph embedding model for cultural domain,” ACM Journal on Computing and Cultural Heritage, vol. 17, no. 1, pp. 1-11, 2024, doi: 10.1145/3625299.
D. Wang, K. Tang, J. Zeng, Y. Pan, Y. Dai, H. Li, et al., “MM-transformer: A transformer-based knowledge graph link prediction model that fuses multimodal features,” Symmetry, vol. 16, no. 8, pp. 961-973, 2024, doi: 10.3390/sym16080961.
J. Yang, Z. Liu, S. Xiao, C. Li, D. Lian, S. Agrawal, et al., “Graphformers: Gnn-nested transformers for representation learning on textual graph,” Advances in Neural Information Processing Systems, vol. 34, no. 2206, pp. 28798-28810, 2021, doi: 10.48550/arXiv.2105.02605.
Z. Shou, Y. Liu, D. Li, J. Mo, and H. Zhang, “A Bloom Cognitive Hierarchical Classification Model for Chinese Exercises Based on Improved Chinese-RoBERTa-wwm and BiLSTM,” International Journal of Advanced Computer Science & Applications, vol. 15, no. 3, pp. 479-489, 2024, doi: 10.14569/ijacsa.2024.0150349.
P. Feng, B. Zhang, L. Yang, and S. Feng, “Entity-Alignment Interaction Model Based on Chinese RoBERTa,” Applied Sciences, vol. 14, no. 14, pp. 6162-6176, 2024, doi: 10.3390/app14146162.
C. Ozkurt, “Comparative Analysis of State-of-the-Art Q&A Models: BERT, RoBERTa, DistilBERT, and ALBERT on SQuAD v2 Dataset,” Chaos and Fractals, vol. 1, no. 1, pp. 19-30, 2024, doi: 10.69882/adba.chf.2024073.
F. Cai, Q. Hu, R. Zhou, and N. Xiong, “REEGAT: RoBERTa Entity Embedding and Graph Attention Networks Enhanced Sentence Representation for Relation Extraction,” Electronics, vol. 12, no. 11, pp. 2429-2444, 2023, doi: 10.3390/electronics12112429.
E. Hassan, A. S. Talaat, and M. A. Elsabagh, “Intelligent text similarity assessment using Roberta with integrated chaotic perturbation optimization techniques,” Journal of Big Data, vol. 12, no. 1, pp. 164-196, 2025, doi: 10.1186/s40537-025-01233-3.
Y. Luo, H. Li, L. Shi, and X. Wu, “Enhancing graph transformers with hierarchical distance structural encoding,” Advances in Neural Information Processing Systems, vol. 37, no. 1821, pp. 57150-57182, 2024, doi: 10.48550/arXiv.2308.11129.
X. Jiang and J. Chen, “Heterogeneous graphormer for extractive multimodal summarization,” Journal of Intelligent Information Systems, vol. 63, no. 2, pp. 355-373, 2025, doi: 10.1007/s10844-024-00886-5.
J. Liao, X. Zhao, J. Tang, W. Zeng, and Z. Tan, “To hop or not, that is the question: Towards effective multi-hop reasoning over knowledge graphs,” World Wide Web, vol. 24, no. 5, pp. 1837-1856, 2021, doi: 10.1007/s11280-021-00911-5.
L. Muller, D. Kusuma, B. Bonet, and C. Morris, “Towards principled graph transformers,” Advances in Neural Information Processing Systems, vol. 37, no. 4026, pp. 126767-126801, 2024, doi: 10.48550/arXiv.2401.10119.
Z. Hu, J. You, Z. Yang, X. Li, H. Xie, Q. Li, et al., “A Multi-Hop Graph Reasoning Network for Knowledge-Based VQA,” ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 3, pp. 1-23, 2025, doi: 10.1145/3724125.
J. Yu, Q. Su, X. Quan, and J. Yin, “Multi-hop reasoning question generation and its application,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 725-740, 2021, doi: 10.1109/TKDE.2021.3073227.
S. Sahoo, M. Arriola, Y. Schiff, A. Gokaslan, E. Marroquin, J. Chiu, et al., “Simple and effective masked diffusion language models,” Advances in Neural Information Processing Systems, vol. 37, no. 4135, pp. 130136-130184, 2024, doi: 10.48550/arXiv.2406.07524.
D. Mizrahi, R. Bachmann, O. Kar, T. Yeo, M. Gao, A. Dehghan, et al., “4M: Massively multimodal masked modeling,” Advances in Neural Information Processing Systems, vol. 36, no. 2544, pp. 58363-58408, 2023, doi: 10.48550/arXiv.2312.06647.
D. Song, F. Zhang, M. Lu, S. Yang, and H. Huang, “DTransE: Distributed translating embedding for knowledge graph,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 10, pp. 2509-2523, 2021, doi: 10.1109/TPDS.2021.3066442.
S. Yao and D. Pi, “A new relational reflection graph convolutional network for the knowledge representation,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 4, pp. 4191-4200, 2023, doi: 10.1007/s12652-023-04516-w.
W. Hu, L. He, H. Ma, K. Wang, and J. Xiao, “Kgner: Improving chinese named entity recognition by bert infused with the knowledge graph,” Applied Sciences, vol. 12, no. 15, pp. 7702-7717, 2022, doi: 10.3390/app12157702.