Context-based Knowledge Discovery and Querying for Social Media Data

Modern Early Warning Systems (EWS) rely on scientific methods to analyse a variety of Earth Observation (EO) and ancillary data provided by multiple and heterogeneous data sources for the prediction and monitoring of hazard events. Furthermore, through social media, the general public can also contribute to the monitoring by reporting warning signs related to hazardous events. However, the warning signs reported by people require additional processing to verify the possibility of the occurrence of hazards. Such processing requires potential data sources to be discovered and accessed. However, the complexity and high variety of these data sources makes this particularly challenging. Moreover, sophisticated domain knowledge of natural hazards and risk management are also required to enable dynamic and timely decision making about serious hazards. In this paper we propose a data integration and analytics system which allows social media users to contribute to hazard monitoring and supports decision making for its prediction. We prototype the system using landslides as an example hazard. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. The system also consists of an interactive agent that allows social media users to report their observations. Using the knowledge modelled within the system, the agent can raise an alert about a potential occurrence of landslides and perform new processes using the data sources suggested by the knowledge base to verify the event.