TY - CPAPER
KW - Computational efficiency
KW - Embeddings
KW - Fast inference
KW - Graph embeddings
KW - High complexity
KW - High-low
KW - Intangible cultural heritages
KW - Knowledge graph
KW - Knowledge graph embedding
KW - Knowledge graphs
KW - Knowledge integration
KW - Knowledge management
KW - Vector spaces
AU - Mao Han
AU - Qing Wang
AU - Hong Chen
AU - Wei Chen
AU - Junchang Zhang
AU - Guang Wang
AB - The construction and effective application of an intangible cultural heritage knowledge graph (ICH KG) can realize the knowledge integration, and optimize the ICH knowledge management. However, high complexity and low computational efficiency of ICH KG make its application face challenges. We propose a multi-source knowledge graph embedding (KGE) model named ICHMKGE to convert the ICH KG into the vector representations to improve the computational efficiency of ICH KG and promote the digital sustainable development of ICH. Firstly, we take the Chinese ICH project 24 solar terms as an example and combine multiple official data sources to construct the Chinese ICH KG as a basis for this study. Secondly, as the ICH project is being further explored with the limited coverage of ICH knowledge, entity sparsity poses a serious challenge for ICH KGE. This paper employs the BERT model to encode the complete description information of entities, and establishes connection between triples entities and ontology concepts via cross-view modeling. Finally, the proposed ICHMKGE model is compared with the baseline models, and the experimental results demonstrate that the model exhibits superiority.
C2 - Proceedings - 2023 IEEE International Conference on High Performance Computing and Communications, Data Science and Systems, Smart City and Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2023
DO - 10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00102
N1 - Type: Conference paper
N2 - The construction and effective application of an intangible cultural heritage knowledge graph (ICH KG) can realize the knowledge integration, and optimize the ICH knowledge management. However, high complexity and low computational efficiency of ICH KG make its application face challenges. We propose a multi-source knowledge graph embedding (KGE) model named ICHMKGE to convert the ICH KG into the vector representations to improve the computational efficiency of ICH KG and promote the digital sustainable development of ICH. Firstly, we take the Chinese ICH project 24 solar terms as an example and combine multiple official data sources to construct the Chinese ICH KG as a basis for this study. Secondly, as the ICH project is being further explored with the limited coverage of ICH knowledge, entity sparsity poses a serious challenge for ICH KGE. This paper employs the BERT model to encode the complete description information of entities, and establishes connection between triples entities and ontology concepts via cross-view modeling. Finally, the proposed ICHMKGE model is compared with the baseline models, and the experimental results demonstrate that the model exhibits superiority.
SP - 718
EP - 725
TI - Representing the Intangible Cultural Heritage Knowledge Graph with Vector Embedding
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189856878&doi=10.1109%2fHPCC-DSS-SmartCity-DependSys60770.2023.00102&partnerID=40&md5=6fc5c44cf88c5cf13509691e16b4dbd1
ER -