Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

中文翻译：

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知识图谱调查：表示，获取和应用。

人类知识提供了对世界的正式理解。表示实体之间结构关系的知识图谱已成为面向认知和人类水平智能的越来越流行的研究方向。在本次调查中，我们对知识图进行了全面回顾，涵盖了有关以下方面的总体研究主题：1）知识图表示学习；2）知识的获取与完善；3）时间知识图；4）知识感知的应用程序，并总结了最近的突破和展望方向，以促进未来的研究。我们提出了关于这些主题的全貌分类和新分类法。知识图嵌入从表示空间，评分功能，编码模型和辅助信息四个方面进行组织。为了获取知识，特别是知识图的完成，嵌入方法，路径推论和逻辑规则推理。我们将进一步探讨几个新兴主题，包括元关系学习，常识推理和时态知识图。为了促进将来对知识图的研究，我们还提供了精选的数据集和针对不同任务的开源库。最后，我们对几个有前途的研究方向有一个全面的展望。