Research on Big Data-driven Knowledge Graph Construction Technology for Intangible Cultural Heritage Digital Resources

With the digitization of intangible cultural heritage (ICH), a large number of ICH digital resources have been created and accumulated. In this paper, BERT-CNN-BiLSTM-CRF information recognition model is proposed for obtaining metadata of ICH digital resources. Then a two-stage mapping approach is utilized to construct the knowledge graph of ICH digital resources. That is, metadata mapping to construct knowledge ontology, followed by mapping to knowledge graph through knowledge ontology. After the model performance test and knowledge graph construction, it can be seen that the spatial distribution of national-level ICH in China is mainly concentrated in the east and west regions. The F1 value of the BERT-CNN-BiLSTM-CRF model is 0.922, which is a better performance for the basic information extraction task compared with other models. The knowledge graph visualizes 7 types of entity nodes of ICH projects, digital resources, organizations, things, people, places, and time, which promotes the inheritance of ICH and knowledge sharing.