论文标题
知识图
Knowledge Graphs
论文作者
论文摘要
在本文中，我们对知识图进行了全面的介绍，这些介绍最近在需要利用多样化，动态，大规模的数据收集的情况下引起了行业和学术界的极大关注。经过一些开幕词，我们激励和对比了用于知识图的各种基于图的数据模型和查询语言。我们在知识图中讨论模式，身份和上下文的作用。我们解释了如何使用演绎技术和电感技术的组合来表示知识和提取知识。我们总结了知识图的创建，丰富，质量评估，改进和发布的方法。我们提供了突出的开放知识图和企业知识图，它们的应用程序以及它们如何使用上述技术的概述。我们以知识图的高级未来研究方向结束。
In this paper 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 and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.
