  Event linking connects event mentions in text with relevant nodes in a
knowledge base (KB). Prior research in event linking has mainly borrowed
methods from entity linking, overlooking the distinct features of events.
Compared to the extensively explored entity linking task, events have more
complex structures and can be more effectively distinguished by examining their
associated arguments. Moreover, the information-rich nature of events leads to
the scarcity of event KBs. This emphasizes the need for event linking models to
identify and classify event mentions not in the KB as ``out-of-KB,'' an area
that has received limited attention. In this work, we tackle these challenges
by introducing an argument-aware approach. First, we improve event linking
models by augmenting input text with tagged event argument information,
facilitating the recognition of key information about event mentions.
Subsequently, to help the model handle ``out-of-KB'' scenarios, we synthesize
out-of-KB training examples from in-KB instances through controlled
manipulation of event arguments. Our experiment across two test datasets showed
significant enhancements in both in-KB and out-of-KB scenarios, with a notable
22% improvement in out-of-KB evaluations.
