摘要:知识图谱是人工智能的重要基石.各领域大规模知识图谱的构建和发布对知识图谱数据管理提出了新的挑战.以数据模型的结构和操作要素为主线，对目前的知识图谱数据管理理论、方法、技术与系统进行研究综述.首先，介绍知识图谱数据模型，包括RDF图模型和属性图模型，介绍5种知识图谱查询语言，包括SPARQL、Cypher、Gremlin、PGQL和G-CORE；然后，介绍知识图谱存储管理方案，包括基于关系的知识图谱存储管理和原生知识图谱存储管理；其次，探讨知识图谱上的图模式匹配、导航式和分析型3种查询操作.同时，介绍主流的知识图谱数据库管理系统，包括RDF三元组库和原生图数据库，描述目前面向知识图谱的分布式系统与框架，给出知识图谱评测基准.最后，展望知识图谱数据管理的未来研究方向.



Abstract:Knowledge graphs have become the cornerstone of artificial intelligence. The construction and publication of large-scale knowledge graphs in various domains have posed new challenges on the data management of knowledge graphs. In this paper, in accordance with the structural and operational elements of a data model, the current theories, methods, technologies, and systems of knowledge graph data management are surveyed. First, the paper introduces knowledge graph data models, including the RDF graph model and the property graph model, and also introduces 5 knowledge graph query languages, including SPARQL, Cypher, Gremlin, PGQL, and G-CORE. Second, the storage management schemes of knowledge graphs are presented, including relational-based and native approaches. Third, three kinds of query operations are discussed, which are graph pattern matching, navigational, and analytical queries. Fourth, the paper introduces mainstream knowledge graph database management systems, which are categorized into RDF triple stores and native graph databases. Meanwhile, the state-of-the-art distributed systems and frameworks that are used for processing knowledge graphs are also described, and benchmarks are presented for knowledge graphs. Finally, the future research directions of knowledge graph data management are put forward as well.



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