The DOI matches its Crossref record; 151 of the 151 references it cites were found in a registry or an index.
Multi-Modal Knowledge Graph Construction and Application: A Survey
Open accessAbstract
Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strengths and weaknesses of different solutions. We finalize this survey with open research problems relevant to MMKGs.
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Cites
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Cited by
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
Multimodal learning with transformers: A survey
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Self-citationKnowledge Graphs: Opportunities and Challenges
Peng, C., Xia, F., Naseriparsa, M., & Osborne, F. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10465-9 · Cited by 717
LLMs for knowledge graph construction and reasoning: recent capabilities and future opportunities
Zhu, Y., Wang, X., Chen, J., Qiao, S., Ou, Y., Yao, Y., Deng, S., Chen, H., & Zhang, N. (2024) · World Wide Web · doi:10.1007/s11280-024-01297-w · Cited by 294
“Knowledge Graph (KG) is a semantic network comprising entities, concepts, and relations (Cai et al., 2022; Chen et al., 2023; Zhu et al., 2022; Liang et al., 2022), which can catalyse applications across various scenarios such as recommendation systems, search engines, and question-answering systems (Zhang et al.”
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Metadata
- Authors
- Zhu, X., Li, Z., Wang, X., Jiang, X., Sun, P., Wang, X., Xiao, Y., & Yuan, N. J.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2024-02
- Volume
- 36
- Issue
- 2
- Pages
- 715-735
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W4312044727
- Cited by
- 291
- References
- 151
- FWCI
- 17.97
- Type
- journal article
- Language
- en
- Open access
- green
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