知识图谱综述——表示、构建、推理与知识超图理论

针对知识图谱（KG）在知识驱动的人工智能研究中发挥的强大支撑作用，分析并总结了现有知识图谱和知识超图技术。首先，从知识图谱的定义与发展历程出发，介绍了知识图谱的分类和架构；其次，对现有的知识表示与存储方式进行了阐述；然后，基于知识图谱的构建流程，分析了各类知识图谱构建技术的研究现状。特别是针对知识图谱中的知识推理这一重要环节，分析了基于逻辑规则、嵌入表示和神经网络的三类典型的知识推理方法。此外，以异构超图引出知识超图的研究进展，并提出三层架构的知识超图，从而更好地表示和提取超关系特征，实现对超关系数据的建模及快速的知识推理。最后，总结了知识图谱和知识超图的典型应用场景并对未来的研究作出了展望。

Knowledge graph survey:representation,construction,reasoning and knowledge hypergraph theory

Knowledge Graph (KG) strongly support the research of knowledge-driven artificial intelligence. Aiming at this fact, the existing technologies of knowledge graph and knowledge hypergraph were analyzed and summarized. At first, from the definition and development history of knowledge graph, the classification and architecture of knowledge graph were introduced. Second, the existing knowledge representation and storage methods were explained. Then, based on the construction process of knowledge graph, several knowledge graph construction techniques were analyzed. Specifically, aiming at the knowledge reasoning, an important part of knowledge graph, three typical knowledge reasoning approaches were analyzed, which are logic rule-based, embedding representation-based, and neural network-based. Furthermore, the research progress of knowledge hypergraph was introduced along with heterogeneous hypergraph. To effectively present and extract hyper-relational characteristics and realize the modeling of hyper-relation data as well as the fast knowledge reasoning, a three-layer architecture of knowledge hypergraph was proposed. Finally, the typical application scenarios of knowledge graph and knowledge hypergraph were summed up, and the future researches were prospected.