Knowledge-enhanced event relation extraction via event ontology prompt

Abstract

Identifying temporal and subevent relationships between different events (i.e., event relation extraction) is an important step towards event-centric natural language processing, which can help understand how events evolve and potentially facilitate many downstream tasks, such as timeline generation and event knowledge graph construction. Existing work has extensively leveraged external knowledge to improve the performance of relation extraction. Despite the progress made, the current knowledge-enhanced approach still has some shortcomings, e.g., knowledge missing, knowledge noise, and suboptimal knowledge injection. In this paper, we propose OntoEnhance, a novel event relation extraction framework that fuses semantic information from event ontologies to enhance event representation. Specifically, we first inject the latent knowledge in the event ontology into the prompt text to address the issue of knowledge missing. Then a dual-stack attention fusion mechanism is further introduced to enhance the injection of key knowledge to alleviate knowledge noise. In order to prevent the knowledge in the event ontology from being wrongly dominated, we use the event direction induction mechanism to obtain the event context-based relational sequence representation. Finally, a gate mechanism is used to fuse ontology-based knowledge and context-based event features dynamically. Extensive experiments demonstrate that OntoEnhance outperforms all comparison baselines by a large margin on all four datasets under both standard and few-shot settings.