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Open Access

Exploring the Chameleon Effect of Contextual Dynamics in Temporal Knowledge Graph for Event Prediction

University of Electronic Science and Technology of China, Chengdu 610054, China. Fan Zhou is also affiliated with Intelligent Terminal Key Laboratory of Sichuan Province, yibin 644000, Sichuan
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Abstract

The ability to forecast future events brings great benefits for society and cyberspace in many public safety domains, such as civil unrest, pandemics and crimes. The occurrences of new events are often correlated or dependent on historical and concurrent events. Many existing studies learn event-occurring processes with sequential and structural models, which, however, suffer from inefficient and inaccurate prediction problems. To better understand the event forecasting task and characterize the occurrence of new events, we exploit the human cognitive theory from the cognitive neuroscience discipline to find available cues for algorithm design and event prediction. Motivated by the dual process theory, we propose a two-stage learning scheme for event knowledge mining and prediction. First, we screen out event candidates based on historical inherent knowledge. Then we re-rank event candidates by probing into the newest relative events. Our proposed model mimics a sociological phenomenon called “the chameleon effect” and consists of a new target attentive graph collaborative learning mechanism to ensure a better understanding of sophisticated evolution patterns associated with events. In addition, self-supervised contrastive learning is employed to alleviate the over-smoothing problem that existed in graph learning while improving the model’s interpretability. Experiments show the effectiveness of our approach.

Keywords

temporal knowledge graphevent forecastinggraph neural networksself-supervised learningexplainability

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Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 433-455
DOI: 10.26599/TST.2024.9010067
Cite this article:
Liu X, He Y, Tai W, et al. Exploring the Chameleon Effect of Contextual Dynamics in Temporal Knowledge Graph for Event Prediction. Tsinghua Science and Technology, 2025, 30(1): 433-455. https://doi.org/10.26599/TST.2024.9010067

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Received: 21 December 2023
Revised: 03 February 2024
Accepted: 01 April 2024
Published: 11 September 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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