Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for innovation. In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm. We begin by defining KGs and MMKGs, then explore their construction progress. Our review includes two primary task categories: KG-aware multi-modal learning tasks, such as Image Classification and Visual Question Answering, and intrinsic MMKG tasks like Multi-modal Knowledge Graph Completion and Entity Alignment, highlighting specific research trajectories. For most of these tasks, we provide definitions, evaluation benchmarks, and additionally outline essential insights for conducting relevant research. Finally, we discuss current challenges and identify emerging trends, such as progress in Large Language Modeling and Multi-modal Pre-training strategies. This survey aims to serve as a comprehensive reference for researchers already involved in or considering delving into KG and multi-modal learning research, offering insights into the evolving landscape of MMKG research and supporting future work.

翻译：知识图谱（KGs）在推动各类人工智能应用中发挥着关键作用，而语义网社区对多模态维度的探索为创新开辟了新途径。本综述仔细审阅了300余篇论文，聚焦于知识图谱感知研究的两个主要方面：知识图谱驱动多模态（KG4MM）学习（即KGs支持多模态任务）与多模态知识图谱（MM4KG）（即将KG研究扩展至MMKG领域）。我们首先定义KGs与MMKGs，继而探讨其构建进展。综述涵盖两类主要任务：知识图谱感知的多模态学习任务（如图像分类与视觉问答）以及MMKG内在任务（如多模态知识图谱补全与实体对齐），并重点阐述了具体研究路径。针对大多数任务，我们提供了定义、评估基准，并额外概述了开展相关研究的关键见解。最后，我们讨论了当前挑战并识别了新兴趋势，例如大规模语言建模与多模态预训练策略的进展。本综述旨在为已参与或考虑涉足知识图谱与多模态学习领域的研究人员提供全面参考，揭示MMKG研究的演进图景，并为未来工作提供支撑。