Future is not One-dimensional: Graph Modeling based Complex Event Schema
  Induction for Event Prediction
  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 either focuses on atomic
events or linear temporal event sequences, ignoring the interplay between
events via arguments and argument relations. We introduce the concept of
Temporal Complex Event Schema: a graph-based schema representation that
encompasses events, arguments, temporal connections and argument relations.
Additionally, we propose a Temporal Event Graph Model that models the emergence
of 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 manually constructed gold schemas.
Intrinsic evaluation by 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 event prediction
further demonstrates the predictive power of our event graph model,
significantly surpassing human schemas and baselines by more than 17.8% on
HITS@1.
