EventKG: A Multilingual Event-Centric Temporal Knowledge Graph

  • Simon GottschalkEmail author
  • Elena Demidova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)


One of the key requirements to facilitate semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. EventKG presented in this paper is a multilingual event-centric temporal knowledge graph that addresses this gap. EventKG incorporates over 690 thousand contemporary and historical events and over 2.3 million temporal relations extracted from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical representation.


Knowledge Graph (KGs) Wikidata DBpedia Effects Information Center Simple Event Model (SEM) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially funded by the ERC (“ALEXANDRIA”, 339233) and BMBF (“Data4UrbanMobility”, 02K15A040).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.L3S Research CenterLeibniz Universität HannoverHannoverGermany

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