Visualizing and Curating Knowledge Graphs over Time and Space

Publicly available knowledge repositories, such as Wikipedia and Freebase, benefit significantly from volunteers, whose contributions ensure that the knowledge keeps expanding and is kept up-to-date and accurate. User interactions are often limited to hypertext, tabular, or graph visualization interfaces. For spatio-temporal information, however, other interaction paradigms may be better-suited. We present an integrated system that combines crowdsourcing, automatic or semi-automatic knowledge harvesting from text, and visual analytics. It enables users to analyze large quantities of structured data and unstructured textual data from a spatio-temporal perspective and gain deep insights that are not easily observed in individual facts.