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arXiv · 2104.06344

The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

Manling Li·Sha Li·Zhenhailong Wang·Lifu Huang·Kyunghyun Cho·Heng Ji·Jiawei Han·Clare Voss
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Abstract

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.

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Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang, Kyunghyun Cho, Heng Ji, Jiawei Han, Clare Voss. 2021-04-13. The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction. https://arxiv.org/abs/2104.06344

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