Noise-powered Multi-modal Knowledge Graph Representation Framework

• URL: http://arxiv.org/abs/2403.06832v4
• Date: Wed, 15 Jan 2025 06:30:19 GMT
• Title: Noise-powered Multi-modal Knowledge Graph Representation Framework
• Authors: Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen, Wen Zhang,
• Abstract summary: The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph representation learning framework.<n>We propose a novel SNAG method that utilizes a Transformer-based architecture equipped with modality-level noise masking.<n>Our approach achieves SOTA performance across a total of ten datasets, demonstrating its versatility.
• Score: 52.95468915728721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for embedding structured knowledge into multi-modal Large Language Models effectively, alleviating issues like knowledge misconceptions and multi-modal hallucinations. In this work, we explore the efficacy of models in accurately embedding entities within MMKGs through two pivotal tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA). Building on this foundation, we propose a novel SNAG method that utilizes a Transformer-based architecture equipped with modality-level noise masking to robustly integrate multi-modal entity features in KGs. By incorporating specific training objectives for both MKGC and MMEA, our approach achieves SOTA performance across a total of ten datasets, demonstrating its versatility. Moreover, SNAG can not only function as a standalone model but also enhance other existing methods, providing stable performance improvements. Code and data are available at https://github.com/zjukg/SNAG.

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