In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

中文翻译：

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知识图谱

在本文中，我们全面介绍了知识图谱，这些知识图谱最近在需要利用多样化、动态、大规模数据集合的场景中引起了工业界和学术界的极大关注。在一些开场白之后，我们激励和对比了各种基于图的数据模型，以及用于查询和验证知识图的语言。我们解释了如何结合使用演绎和归纳技术来表示和提取知识。我们总结了知识图谱的高层次未来研究方向。