EventKE: Event-Enhanced Knowledge Graph Embedding
Zixuan Zhang, Hongwei Wang, Han Zhao, Hanghang Tong, Heng Ji · 2021
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE.Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network interconnected by event argument links.We then use entity-entity relations from the original KG and event-event temporal links to innerconnect entity and event nodes respectively.We design a novel and effective attentionbased message passing method, which is conducted on entity-entity, event-entity, and eventevent relations to fuse the event information into KG embeddings.Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks. 1