Lifu Huang


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.


Lifu Huang

Publication Details

Date of publication:
April 15, 2021
Cornell University
Publication note:

Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang, Kyunghyun Cho, Heng Ji, Jiawei Han, Clare R. Voss:Future is not One-dimensional: Graph Modeling based Complex Event Schema Induction for Event Prediction. CoRR abs/2104.06344 (2021)