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Abstract
Cultural heritage management poses significant challenges for museums due to fragmented data, limited intelligent frameworks, and insufficient applications. In response, a digital cultural heritage management approach based on knowledge graphs and deep learning algorithms is proposed to address the above challenges. A joint entity-relation triple extraction model is proposed to automatically identify entities and relations from fragmented data for knowledge graph construction. Additionally, a knowledge completion model is presented to predict missing information and improve knowledge graph completeness. Comparative simulations have been conducted to demonstrate the effectiveness and accuracy of the proposed approach for both the knowledge extraction model and the knowledge completion model. The efficacy of the knowledge graph application is corroborated through a case study utilizing ceramic data from the Palace Museum in China. This method may benefit users since it provides automated, interconnected, visually appealing, and easily accessible information about cultural heritage.
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Details
1 Northwestern Polytechnical University, Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Xi’an, China (GRID:grid.440588.5) (ISNI:0000 0001 0307 1240); Delft University of Technology, School of Industrial Design Engineering, Delft, Netherlands (GRID:grid.5292.c) (ISNI:0000 0001 2097 4740)
2 Northwestern Polytechnical University, Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Xi’an, China (GRID:grid.440588.5) (ISNI:0000 0001 0307 1240)
3 Zhejiang University, College of Computer Science and Technology, Hangzhou, China (GRID:grid.13402.34) (ISNI:0000 0004 1759 700X)
4 Northwestern Polytechnical University, Ocean Institute, Taicang, China (GRID:grid.440588.5) (ISNI:0000 0001 0307 1240); Shanghai Jiao Tong University, Department of Automation, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293)