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A Survey of Multi-modal Knowledge Graphs: Technologies and Trends

A comprehensive survey on multi-modal knowledge graphs and their applications.

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A Survey of Multi-modal Knowledge Graphs: Technologies and Trends

By Wanying Liang, Pasquale De Meo, Yong Tang, Jia ZhuACM Computing Surveys
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The paper provides a rigorous definition of multi-modal knowledge graphs (MMKGs) and classifies existing approaches based on four fundamental challenges: representation, fusion, alignment, and translation.

It aims to inspire researchers in the field of artificial intelligence by providing a reference for MMKGs. The survey highlights the potential of MMKGs in handling tasks that standard knowledge graphs cannot process.

Abstract

The paper provides a rigorous definition of multi-modal knowledge graphs (MMKGs) and classifies existing approaches based on four fundamental challenges: representation, fusion, alignment, and translation. It aims to inspire researchers in the field of artificial intelligence by providing a reference for MMKGs. The survey highlights the potential of MMKGs in handling tasks that standard knowledge graphs cannot process.

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multi-modal knowledge graphsknowledge graph applicationsartificial intelligenceknowledge representationKnowledge GraphsStructured ContentContent EngineeringAI Agents
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