EventKG- A Multilingual Event-Centric Temporal Knowledge Graph
published: July 10, 2018, recorded: June 2018, views: 680
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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 aims to address 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 less structured sources and makes this information available through a canonical representation.In this paper we present EventKG including its data model, extraction process, and characteristics and discuss its relevance for several real-world applications including Question Answering, timeline generation and cross-cultural analytics.
Download slides: eswc2018_gottschalk_knowledge_graph_01.pdf (2.5 MB)
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