EventKG: A Multilingual Event-Centric Temporal Knowledge Graph
This addresses the problem of insufficient event representation for semantic analytics in domains like news and social media, though it is incremental as it builds on existing knowledge graphs.
The paper tackles the lack of comprehensive event and temporal relation coverage in existing knowledge graphs by introducing EventKG, a multilingual event-centric temporal knowledge graph that incorporates over 690,000 events and 2.3 million temporal relations from various sources.
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.