Spatiotemporal and semantic information extraction from Web news reports about natural hazards

In the field of geographic information science, modeling geographic dynamics based on spatiotemporal information extracted from the Web, especially unconstructed data such as online news reports, is a growing area of research. Extracting spatiotemporal and semantic information from a set of Web documents enables us to build a rich representation of geographic knowledge described in text, capturing where, when, or what events have occurred. This work investigates the role ontologies play as a key component in the process of semantic information extraction. We show how ontologies can be used in conjunction with natural language gazetteers in order to process semantic information about hazard events and augment spatiotemporal extraction with semantics. We are interested in capturing the spatiotemporal patterns of hazard-related events from online news reports to track the occurrences and evolution of natural hazards, such as severe storms. A hazard ontology has been created to assist the spatiotemporal information extraction process, especially with the automatic detection of different kinds of events at multiple granularities from unstructured texts revealing relationships between the events over space-time. The extraction and retrieval of semantic information about event dynamics provides information about the progression of events using both natural and human perspectives.