In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the context of information retrieval (IR). Modern IR systems can benefit from information available in KGs in multiple ways, independent of whether the KGs are publicly available or proprietary ones. We provide an overview of the components required when building IR systems that leverage KGs and use a taskoriented organization of the material that we discuss. As an understanding of the intersection of IR and KGs is beneficial to many researchers and practitioners, we consider prior work from two complementary angles: leveraging KGs for information retrieval and enriching KGs using IR techniques. We start by discussing how KGs can be employed to support IR tasks, including document and entity retrieval. We then proceed by describing how IR—and language technology in general—can be utilized for the construction and completion of KGs. This includes tasks such as entity recognition, typing, and relation extraction. We discuss common issues that appear across the tasks that we consider and identify future directions for addressing them. We also provide pointers to datasets and other resources that should be useful for both newcomers and experienced researchers in the area.

在此调查中，我们提供了有关信息检索（IR）情况下知识图（KG）的文献的概述。现代的IR系统可以以多种方式从KG中可用的信息中受益，而与KG是公开可用的还是专有的无关。我们提供了构建利用KG并使用我们讨论的材料的面向任务组织的IR系统时所需组件的概述。由于对IR和KG的交集的理解对许多研究人员和从业者都是有益的，因此我们从两个互补的角度考虑先前的工作：利用KG进行信息检索和使用IR技术丰富KG。我们首先讨论如何利用KG来支持IR任务，包括文档和实体检索。然后，我们将介绍如何将IR（以及一般的语言技术）用于KG的构建和完成。这包括诸如实体识别，键入和关系提取之类的任务。我们讨论在我们考虑的任务中出现的常见问题，并确定解决这些问题的未来方向。我们还提供了指向该地区的新手和经验丰富的研究人员都应该有用的数据集和其他资源的指针。