  Event schemas encode knowledge of stereotypical structures of events and
their connections. As events unfold, schemas are crucial to act as a
scaffolding. Previous work on event schema induction focuses either on atomic
events or linear temporal event sequences, ignoring the interplay between
events via arguments and argument relations. We introduce a new concept of
Temporal Complex Event Schema: a graph-based schema representation that
encompasses events, arguments, temporal connections and argument relations. In
addition, we propose a Temporal Event Graph Model that predicts event instances
following the temporal complex event schema. To build and evaluate such
schemas, we release a new schema learning corpus containing 6,399 documents
accompanied with event graphs, and we have manually constructed gold-standard
schemas. Intrinsic evaluations based on schema matching and instance graph
perplexity, prove the superior quality of our probabilistic graph schema
library compared to linear representations. Extrinsic evaluation on
schema-guided future event prediction further demonstrates the predictive power
of our event graph model, significantly outperforming human schemas and
baselines by more than 23.8% on HITS@1.
