Extracting and aggregating temporal events from text

Finding reliable information about a given event from large and dynamic text collections is a topic of great interest. For instance, rescue teams and insurance companies are interested in concise facts about damages after disasters, which can be found in web blogs, newspaper articles, social networks etc. However, finding, extracting, and condensing specific facts is a highly complex undertaking: It requires identifying appropriate textual sources, recognizing relevant facts within the sources, and aggregating extracted facts into a condensed answer despite inconsistencies, uncertainty, and changes over time. In this paper, we present a three-step framework providing techniques and solutions for each of these problems. We tested the feasibility of extracting time-associated event facts using our framework in a comprehensive case study: gathering data on particular earthquakes from web data sources. Our results show that it is, under certain circumstances, possible to automatically obtain reliable and timely data on natural disasters from the web.