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EVIN: Extracting Named Events from News


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.

EVIN Experimental Data

The experimental data can be downloaded here.

The experimental data including the large news corpus used for knowledge base population can be downloaded here (1.4 GB).

If you have any question regarding the data please contact Erdal Kuzey.

Video Demo

Watch the video demo showing the functionality of EVIN. The demo paper is here.

Further Information

EVIN is part of the YAGO-NAGA project at the Max Planck Institute for Informatics in Saarbrücken/Germany. It is developed by the Databases and Information Systems Group.

The people behind EVIN are Erdal Kuzey and Gerhard Weikum. For questions and comments, please contact Erdal Kuzey.