  One of the key requirements to facilitate the 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, entities 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. In this article we address this limitation, formalise
the concept of a temporal knowledge graph and present its instantiation -
EventKG. EventKG is a multilingual event-centric temporal knowledge graph that
incorporates over 690 thousand events and over 2.3 million temporal relations
obtained from several large-scale knowledge graphs and semi-structured sources
and makes them available through a canonical RDF representation. Whereas
popular entities often possess hundreds of relations within a temporal
knowledge graph such as EventKG, generating a concise overview of the most
important temporal relations for a given entity is a challenging task. In this
article we demonstrate an application of EventKG to biographical timeline
generation, where we adopt a distant supervision method to identify relations
most relevant for an entity biography. Our evaluation results provide insights
on the characteristics of EventKG and demonstrate the effectiveness of the
proposed biographical timeline generation method.
