DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph

TL;DR

DyMRL introduces a novel dynamic multispace learning framework that effectively captures deep, time-sensitive multimodal knowledge and evolving fusion features, significantly improving event forecasting accuracy in knowledge graphs.

Contribution

The paper presents DyMRL, a new approach integrating deep relational representations across multiple geometric spaces and dual fusion-evolution attention mechanisms for dynamic multimodal knowledge modeling.

Findings

DyMRL outperforms state-of-the-art methods on four multimodal temporal knowledge graph benchmarks.

It effectively captures deep, time-sensitive structural features from multiple geometric spaces.

DyMRL demonstrates superior event forecasting accuracy compared to static and unimodal baselines.

Abstract

Accurate representation of multimodal knowledge is crucial for event forecasting in real-world scenarios. However, existing studies have largely focused on static settings, overlooking the dynamic acquisition and fusion of multimodal knowledge. 1) At the knowledge acquisition level, how to learn time-sensitive information of different modalities, especially the dynamic structural modality. Existing dynamic learning methods are often limited to shallow structures across heterogeneous spaces or simple unispaces, making it difficult to capture deep relation-aware geometric features. 2) At the knowledge fusion level, how to learn evolving multimodal fusion features. Existing knowledge fusion methods based on static coattention struggle to capture the varying historical contributions of different modalities to future events. To this end, we propose DyMRL, a Dynamic Multispace Representation Learning approach to efficiently acquire and fuse multimodal temporal knowledge. 1) For the former issue, DyMRL integrates time-specific structural features from Euclidean, hyperbolic, and complex spaces into a relational message-passing framework to learn deep representations, reflecting human intelligences in associative thinking, high-order abstracting, and logical reasoning. Pretrained models endow DyMRL with time-sensitive visual and linguistic intelligences. 2) For the latter concern, DyMRL incorporates advanced dual fusion-evolution attention mechanisms that assign dynamic learning emphases equally to different modalities at different timestamps in a symmetric manner. To evaluate DyMRL's event forecasting performance through leveraging its learned multimodal temporal knowledge in history, we construct four multimodal temporal knowledge graph benchmarks. Extensive experiments demonstrate that DyMRL outperforms state-of-the-art dynamic unimodal and static multimodal baseline methods.