RLGNet: Repeating-Local-Global History Network for Temporal Knowledge Graph Reasoning

Abstract

Temporal Knowledge Graph (TKG) reasoning involves predicting future events based on historical information. However, due to the unpredictability of future events, this task is highly challenging. To address this issue, we propose a multi-scale hybrid architecture model based on ensemble learning, called RLGNet (Repeating-Local-Global History Network). Inspired by the application of multi-scale information in other fields, we introduce the concept of multi-scale information into TKG reasoning. Specifically, RLGNet captures and integrates different levels of historical information by combining modules that process information at various scales. The model comprises three modules: the Repeating History Module focuses on identifying repetitive patterns and trends in historical data, the Local History Module captures short-term changes and details, and the Global History Module provides a macro perspective on long-term changes. Additionally, to address the limitations of previous single-architecture models in generalizing across single-step and multi-step reasoning tasks, we adopted architectures based on Recurrent Neural Networks (RNN) and Multi-Layer Perceptrons (MLP) for the Local and Global History Modules, respectively. This hybrid architecture design enables the model to complement both multi-step and single-step reasoning capabilities. Finally, to address the issue of noise in TKGs, we adopt an ensemble learning strategy, combining the predictions of the three modules to reduce the impact of noise on the final prediction results. In the evaluation on six benchmark datasets, our approach generally outperforms existing TKG reasoning models in multi-step and single-step reasoning tasks.