  Event Detection (ED) aims to identify event trigger words from a given text
and classify it into an event type. Most of current methods to ED rely heavily
on training instances, and almost ignore the correlation of event types. Hence,
they tend to suffer from data scarcity and fail to handle new unseen event
types. To address these problems, we formulate ED as a process of event
ontology population: linking event instances to pre-defined event types in
event ontology, and propose a novel ED framework entitled OntoED with ontology
embedding. We enrich event ontology with linkages among event types, and
further induce more event-event correlations. Based on the event ontology,
OntoED can leverage and propagate correlation knowledge, particularly from
data-rich to data-poor event types. Furthermore, OntoED can be applied to new
unseen event types, by establishing linkages to existing ones. Experiments
indicate that OntoED is more predominant and robust than previous approaches to
ED, especially in data-scarce scenarios.
