SimDiff: Simple Denoising Probabilistic Latent Diffusion Model for Data Augmentation on Multi-modal Knowledge Graph

Ran Li, Shimin Di, Lei Chen, Xiaofang Zhou

Abstract: In this paper, we address the challenges of data augmentation in Multi-Modal Knowledge Graphs (MMKGs), a relatively under-explored area. We propose a novel diffusion-based generative model, the Simple Denoising Probabilistic Latent Diffusion Model (SimDiff). SimDiff is capable of handling different data modalities including the graph topology in a unified manner by the same diffusion model in the latent space. It enhances the utilization of multi-modal data and encourage the multi-modal fusion and reduces the dependency on limited training data. We validate our method in downstream Entity Alignment (EA) tasks in MMKGs, demonstrating that even when using only half of the seed entities in training, our methods can still achieve superior performance. This work contributes to the field by providing a new data generation or augmentation method for MMKGs, potentially paving the way for more effective use of MMKGs in various applications. Code is made available at https://github.com/ranlislz/SimDiff.