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，而语义网社区向多模态维度的探索开辟了创新的新途径。本综述仔细回顾了300余篇文献，聚焦于知识图谱感知研究的两个主要方面：知识图谱驱动的多模态学习（KG4MM），其中KGs支持多模态任务；以及多模态知识图谱（MM4KG），它将KG研究扩展到多模态知识图谱（MMKG）领域。我们首先定义KGs和MMKGs，继而探讨其构建进展。综述涵盖两大类任务：知识图谱感知的多模态学习任务（如图像分类和视觉问答）及固有的MMKG任务（如多模态知识图谱补全和实体对齐），并重点介绍了具体的研究路径。针对大多数任务，我们提供定义、评估基准，并额外概述开展相关研究的关键见解。最后，我们讨论当前挑战并识别新兴趋势，例如大语言模型和多模态预训练策略的进展。本综述旨在为已从事或考虑涉足KG与多模态学习的研究人员提供全面参考，揭示MMKG研究的演变格局，并为未来工作提供支持。