In this talk, we present our work on event-centric information extraction and retrieval with an event being simply defined as a combination of spatial and temporal information. For this, we first introduce our multilingual, domain-sensitive temporal tagger HeidelTime and describe challenges occurring when extracting and normalizing temporal expressions from text documents of different domains. Then, we present our proximity-aware ranking model for spatio-temporal information retrieval, which allows to rank search results given queries with topical, temporal, and geographic constraints. Finally, we introduce our event search engine with its map-based exploration features.