论文标题
知识图构造的GEOAI：确定级联事件之间的因果关系以支持环境弹性研究
GeoAI for Knowledge Graph Construction: Identifying Causality Between Cascading Events to Support Environmental Resilience Research
论文作者
论文摘要
知识图技术被认为是一种功能强大且启用语义的解决方案，可以链接实体，从而使用户可以根据各种推理规则来推理数据来得出新知识。但是，在构建这样的知识图时，事件建模（例如灾难的事件）通常仅限于单个孤立的事件。级联事件之间的联系通常在现有知识图中缺少。本文介绍了我们的GEOAI（地理空间人工智能）解决方案，以根据一组空间和时间启用的语义规则确定事件之间的因果关系，尤其是灾难事件。通过因果灾难事件建模的用例，我们演示了这些定义的规则，包括基于主题的相关事件的识别，时空同时存在约束以及事件元数据的文本挖掘，可以自动提取不同事件之间的因果关系。我们的解决方案丰富了事件知识库，并允许在大型知识图中探索链接的级联事件，从而授权知识查询和发现。
Knowledge graph technology is considered a powerful and semantically enabled solution to link entities, allowing users to derive new knowledge by reasoning data according to various types of reasoning rules. However, in building such a knowledge graph, events modeling, such as that of disasters, is often limited to single, isolated events. The linkages among cascading events are often missing in existing knowledge graphs. This paper introduces our GeoAI (Geospatial Artificial Intelligence) solutions to identify causality among events, in particular, disaster events, based on a set of spatially and temporally-enabled semantic rules. Through a use case of causal disaster events modeling, we demonstrated how these defined rules, including theme-based identification of correlated events, spatiotemporal co-occurrence constraint, and text mining of event metadata, enable the automatic extraction of causal relationships between different events. Our solution enriches the event knowledge base and allows for the exploration of linked cascading events in large knowledge graphs, therefore empowering knowledge query and discovery.
