Research on Entity Relationships in the Knowledge Graph of Disease Monitoring in Grotto Temples

Abstract

Abstract. The grotto temple, carved into cliffs and widely distributed, is a significant cultural heritage in China. However, it faces severe damage and collapse threats due to natural disaster risks in its environment. Nearly seventy percent of grotto temples are located in regions prone to earthquakes and water hazards, leading to varying degrees of damage to cultural artifacts. Therefore, preventive measures are necessary to reduce the impact of natural disasters on grotto temples. A knowledge graph, a structured semantic knowledge base describing concepts and their relationships in the physical world, plays a crucial role in knowledge organization and content representation. Entity relationships are the core of knowledge, serving as both foundational data and a key task in constructing knowledge graphs and processing unstructured text. In the field of grotto temple disease monitoring, while data continues to grow, research on the correlation between textual data remains underexplored. This paper adopts the BiLSTM-CRF method to extract entity relationships, matching them with the grotto temple monitoring knowledge graph. Finally, the Neo4j software is utilized to program and display the knowledge graph, aiming to enhance the efficiency of natural disaster risk management and cultural heritage protection for grotto temples.