Open Access

ARTICLE

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Zhipeng Li^1,2, Shanshan Feng^3,*, Jun Shi², Yang Zhou², Yong Liao^1,2, Yangzhao Yang², Yangyang Li⁴, Nenghai Yu¹, Xun Shao⁵

1 University of Science and Technology of China, Hefei, 230027, China
2 Shenzhen CyberAray Co., Ltd., Shenzhen, 518038, China
3 Harbin Institute of Technology, Shenzhen, 518055, China
4 National Engineering Research Center for Risk Perception and Prevention (RPP), Beijing, 100041, China
5 School of Regional Innovation and Social Design Engineering, Kitami Institute of Technology, Kitami, 090-8507, Japan

* Corresponding Author: Shanshan Feng. Email:

Computer Systems Science and Engineering 2023, 44(3), 2411-2423. https://doi.org/10.32604/csse.2023.026823

Received 05 January 2022; Accepted 25 March 2022; Issue published 01 August 2022

Abstract

Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs and predict events happening in the future. To address this problem, some recent works learn to infer future events based on historical event-based temporal knowledge graphs. However, these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously. This paper proposes a new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently. More specifically, our RE-GAT uses an attention-based historical events embedding module to encode past events, and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp. A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations. We evaluate our proposed method on four benchmark datasets. Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various baselines, which proves that our method can more accurately predict what events are going to happen.

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Keywords

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