Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction

TL;DR

This paper introduces an adaptive pseudo-siamese policy network leveraging reinforcement learning to improve temporal knowledge prediction by modeling evolutionary patterns and handling seen and unseen entities.

Contribution

It proposes a novel adaptive pseudo-siamese policy network with two sub-networks and a gating mechanism for unified temporal knowledge prediction, addressing key challenges in the field.

Findings

Achieves superior performance on four benchmark datasets.

Effectively models temporal evolutionary patterns.

Handles unseen entities within a unified framework.

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.