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Multi-Modal Knowledge Graph Construction and Application: A Survey

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arxiv 2202.05786 v2 pith:KH2OFWUY submitted 2022-02-11 cs.AI cs.CLcs.CV

Xiangru Zhu , Zhixu Li , Xiaodan Wang , Xueyao Jiang , Penglei Sun , Xuwu Wang , Yanghua Xiao , Nicholas Jing Yuan This is my paper
classification cs.AIcs.CLcs.CV
keywords knowledgemmkgsgraphsmulti-modalsurveyapplicationconstructionmachine
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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 strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.

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Cited by 2 Pith papers

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  1. Evidence-Grounded Multimodal Knowledge Graph Construction for Multi-Lecture Educational Reasoning

    cs.AI 2026-08 conditional novelty 5.0 of 10

    A pipeline that transcribes lecture videos, reads slides and diagrams, and builds an evidence-linked knowledge graph, tested on three neural-network lectures with a three-question sanity check.

  2. MemVerse: Multimodal Memory for Lifelong Learning Agents

    cs.AI 2025-12 reject novelty 4.0 of 10

    MemVerse reports large gains on multimodal benchmarks by adding a hierarchical knowledge-graph memory plus fine-tuned parametric recall, but its strongest video-retrieval result uses ground-truth caption-video pairs i...

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