Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in the area of the Semantic Web as well as gaining popularity in other application domains such as data mining and search engines. Simultaneously, there has been enormous progress in the development of different types of heterogeneous hardware, impacting the way KGs are processed. The aim of this paper is to provide a systematic literature review of knowledge graph hardware acceleration. For this, we present a classification of the primary areas in knowledge graph technology that harnesses different hardware units for accelerating certain knowledge graph functionalities. We then extensively describe respective works, focusing on how KG related schemes harness modern hardware accelerators. Based on our review, we identify various research gaps and future exploratory directions that are anticipated to be of significant value both for academics and industry practitioners.

翻译：近年来，知识图谱（KGs）受到了广泛关注，特别是在语义网领域，同时在数据挖掘和搜索引擎等其他应用领域也日益普及。与此同时，各类异构硬件的开发取得了巨大进展，影响了知识图谱的处理方式。本文旨在对知识图谱硬件加速进行系统的文献综述。为此，我们首先对知识图谱技术中利用不同硬件单元以加速特定知识图谱功能的主要领域进行分类。随后，我们详细阐述了相关研究工作，重点关注知识图谱相关方案如何利用现代硬件加速器。基于本次综述，我们指出了多个研究空白和未来的探索方向，预计这些方向对学术界和工业界从业者都具有重要价值。