论文标题

部分可观测时空混沌系统的无模型预测

Multi-Modal Knowledge Graph Construction and Application: A Survey

论文作者

Zhu, Xiangru, Li, Zhixu, Wang, Xiaodan, Jiang, Xueyao, Sun, Penglei, Wang, Xuwu, Xiao, Yanghua, Yuan, Nicholas Jing

论文摘要

近年来见证了知识工程的复兴,这是知识图的快速增长。但是,大多数现有知识图都用纯符号表示,这损害了机器理解现实世界的能力。知识图的多模式化是迈向实现人类级机器智能的不可避免的关键步骤。这项工作的结果是多模式知识图(MMKGS)。在这项关于由文本和图像构建的MMKG的调查中,我们首先给出MMKGS的定义,然后对多模式任务和技术进行初步。然后,我们分别系统地回顾了MMKGS的构建和应用的挑战,进步和机会,并详细分析了不同解决方案的实力和弱点。我们通过与MMKGS相关的开放研究问题来最终确定这项调查。

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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