Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction

• URL: http://arxiv.org/abs/2204.12036v1
• Date: Tue, 26 Apr 2022 02:17:39 GMT
• Title: Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction
• Authors: Pengpeng Shao, Tong Liu, Feihu Che, Dawei Zhang, Jianhua Tao
• Abstract summary: We propose a novel adaptive pseudo-siamese policy network for temporal knowledge prediction based on reinforcement learning. In sub-policy network I, the agent searches for the answer for the query along the entity-relation paths to capture the static evolutionary patterns. In sub-policy network II, the agent searches for the answer for the query along the relation-time paths to deal with unseen entities.
• Score: 37.36680021388575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using relevant historical facts on the temporal knowledge graphs. There are two main difficulties in this prediction task. First, from the historical facts point of view, how to model the evolutionary patterns of the facts to predict the query accurately. Second, from the query perspective, how to handle the two cases where the query contains seen and unseen entities in a unified framework. Driven by the two problems, we propose a novel adaptive pseudo-siamese policy network for temporal knowledge prediction based on reinforcement learning. Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks. In sub-policy network I, the agent searches for the answer for the query along the entity-relation paths to capture the static evolutionary patterns. And in sub-policy network II, the agent searches for the answer for the query along the relation-time paths to deal with unseen entities. Moreover, we develop a temporal relation encoder to capture the temporal evolutionary patterns. Finally, we design a gating mechanism to adaptively integrate the results of the two sub-policy networks to help the agent focus on the destination answer. To assess our model performance, we conduct link prediction on four benchmark datasets, the experimental results demonstrate that our method obtains considerable performance compared with existing methods.

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