Construction Method of Knowledge Graph of Chain Disaster in Alpine Gorge Area, China

In high-mountain canyon areas, complex geological environments lead to frequent cascading disasters with unclear triggering mechanisms, posing severe threats to human life and property. Existing knowledge graph research in geology predominantly focuses on single-hazard types or general geological entities, lacking structured modeling and specialized datasets for cascading disaster processes, particularly the evolutionary chains in high-mountain canyon settings. To address this gap, this study proposes a method for constructing a knowledge graph tailored to cascading disasters in high-mountain canyon regions. First, a three-layer schema framework—comprising concept, relation, and instance layers—was designed to systematically characterize the knowledge elements and evolutionary relationships of disaster chains. To address the lack of a knowledge dataset for cascade disasters, this paper integrates multi-source heterogeneous data to construct a high-mountain canyon cascading disasters entity–relation dataset (DCER-MC), providing a reliable benchmark for related tasks. Based on this dataset, we implemented the knowledge graph and conducted disaster chain analysis. Experiments and applications demonstrate that the constructed knowledge graph effectively supports structured storage, centralized management, and scenario-based application of regional cascading disaster information. The main contributions of this work are (1) proposing a targeted schema framework for cascading-disaster knowledge graphs; (2) releasing a specialized dataset for cascading disasters in high-mountain canyon regions; and (3) establishing a complete pipeline from data to knowledge to scenario-based services, offering a novel knowledge-driven paradigm for disaster chain risk identification, inference prediction, and emergency decision-making in these areas.