  Event schemas are a form of world knowledge about the typical progression of
events. Recent methods for event schema induction use information extraction
systems to construct a large number of event graph instances from documents,
and then learn to generalize the schema from such instances. In contrast, we
propose to treat event schemas as a form of commonsense knowledge that can be
derived from large language models (LLMs). This new paradigm greatly simplifies
the schema induction process and allows us to handle both hierarchical
relations and temporal relations between events in a straightforward way. Since
event schemas have complex graph structures, we design an incremental prompting
and verification method to break down the construction of a complex event graph
into three stages: event skeleton construction, event expansion, and
event-event relation verification. Compared to directly using LLMs to generate
a linearized graph, our method can generate large and complex schemas with 7.2%
F1 improvement in temporal relations and 31.0% F1 improvement in hierarchical
relations. In addition, compared to the previous state-of-the-art closed-domain
schema induction model, human assessors were able to cover $\sim$10% more
events when translating the schemas into coherent stories and rated our schemas
1.3 points higher (on a 5-point scale) in terms of readability.

Computation and Language