Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913243537080320 |
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| author | Chen, Zhuo Zhang, Yichi Fang, Yin Geng, Yuxia Guo, Lingbing Chen, Xiang Li, Qian Zhang, Wen Chen, Jiaoyan Zhu, Yushan Li, Jiaqi Liu, Xiaoze Pan, Jeff Z. Zhang, Ningyu Chen, Huajun |
| author_facet | Chen, Zhuo Zhang, Yichi Fang, Yin Geng, Yuxia Guo, Lingbing Chen, Xiang Li, Qian Zhang, Wen Chen, Jiaoyan Zhu, Yushan Li, Jiaqi Liu, Xiaoze Pan, Jeff Z. Zhang, Ningyu Chen, Huajun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey Chen, Zhuo Zhang, Yichi Fang, Yin Geng, Yuxia Guo, Lingbing Chen, Xiang Li, Qian Zhang, Wen Chen, Jiaoyan Zhu, Yushan Li, Jiaqi Liu, Xiaoze Pan, Jeff Z. Zhang, Ningyu Chen, Huajun Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Machine Learning 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. |
| title | Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2402.05391 |