EVIN (EVents In News) is a system that can extract named events from a news corpus, organizes them into ontological classes, and supports interactive exploration. EVIN exploits different kinds of similarities between news items referring to textual contents, entity occurrences, and temporal ordering, and captures these similarities in a multi-view attributed graph. To distill canonicalized events that can serve to populate a clean ontology, EVIN coarsens the graph by iterative merging based on a judiciously designed loss function. In this demo, we show how EVIN can extract named events on-the-fly based on a user's specific interests. EVIN provides a GUI that allows users to query the system, to browse the extracted events along a timeline visualization, and to explore details about events and the associated news.

For scientific works about EVIN, cite this paper.