  Event series, such as the Wimbledon Championships and the US presidential
elections, represent important happenings in key societal areas including
sports, culture and politics. However, semantic reference sources, such as
Wikidata, DBpedia and EventKG knowledge graphs, provide only an incomplete
event series representation. In this paper we target the problem of event
series completion in a knowledge graph. We address two tasks: 1) prediction of
sub-event relations, and 2) inference of real-world events that happened as a
part of event series and are missing in the knowledge graph. To address these
problems, our proposed supervised HapPenIng approach leverages structural
features of event series. HapPenIng does not require any external knowledge -
the characteristics making it unique in the context of event inference. Our
experimental evaluation demonstrates that HapPenIng outperforms the baselines
by 44 and 52 percentage points in terms of precision for the sub-event
prediction and the inference tasks, correspondingly.
