Event Relation Extraction Based on Heterogeneous Graph Attention Networks and Event Ontology Direction Induction

Event relation extraction plays a crucial role in constructing an event knowledge graph. However, current models only extract trigger words as event ontology representations, and do not consider node type during information aggregation, resulting in low accuracy in event relation extraction. To address these challenges, we propose an event relation extraction model based on heterogeneous graph attention networks and event ontology direction induction. To enhance the completeness of event information, we incorporate argument role information, in addition to trigger words, into the input text. A novel heterogeneous graph attention framework is proposed to reasonably allocate weights to trigger words, argument roles, and text information, and then perform two levels of aggregation, node-level and semantic-level, in sequence. To improve the accuracy of event direction discrimination, we construct an event ontology subgraph that includes trigger words and arguments to aggregate complete event structure information during direction induction. Finally, we evaluate our model on three datasets, TimeBank-Dense, MATRES, and HiEve, and demonstrate that our model outperforms state-of-the-art models by 1.2%, 0.5%, and 0.8%, respectively, in terms of the Micro-F1 score. Our proposed model provides a promising solution for event relation extraction and can be applied in various natural language processing applications.