Abstract. Natural disasters have a significant impact on the environment and economies of all countries around the world, and a large amount of multi-source heterogeneous geographic information data is generated every day. However, due to a lack of knowledge transformation capabilities, these nations continue to struggle with the issue of "a large amount of data and little knowledge". Therefore, it is of great significance how to extract geographic knowledge related to disasters from the vast data and construct a geographic knowledge graph integrating disaster information. Based on the theory related to knowledge extraction, this paper proposes a method to construct a natural disaster knowledge graph integrating geographic information. The core of this knowledge graph is to construct the association relationship between natural disaster concepts, research areas, and spatial data. The vocabulary and relationships associated with disaster concepts are primarily transformed by an existing word list of geographic narratives, which then provide rich semantic relationships of domain concepts for the entire knowledge graph. The research areas and spatial data types are mainly obtained through knowledge entity extraction and disambiguation methods. This disaster knowledge graph can support applications well such as natural disaster visualization and analysis, data recommendation systems, and intelligent Q&A systems, which can further improve the intelligence of natural disaster knowledge services and is expected to promote the sharing and reuse of domain knowledge graphs to a certain extent.
Abstract. The analysis of natural hazards is still lacking in real-time and comprehensiveness. To enhance the intelligent analysis of disasters, reduce natural disasters, and provide more timely and accurate disaster warnings. The method of constructing a natural disaster research event graph using the abstracts of research literature related to natural disaster analysis as the data source is proposed. First, a rule-based matching algorithm combined with syntactic analysis is used to extract the events of the disaster analysis process and spatio-temporal information from the abstract; Then build an event storage model based on Neo4j to store the event chain and related information; Then use Word2vec to convert events into word vectors, and define the event similarity as a linear sum of the object similarity and predicate similarity of the two events, combine event similarity setting equation to calculate core nodes and perform event generalization and fusion of graphs. Based on the above method, soil erosion is used as an example to construct an event graph and provide the basis for decision making services for intelligent analysis of natural disasters.
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