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| 知识图谱数据管理研究综述 |
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王鑫1,2, 邹磊3, 王朝坤4, 彭鹏5, 冯志勇1,2
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1.天津大学 智能与计算学部, 天津 300350;2.天津市认知计算与应用重点实验室, 天津 300350;3.北京大学 计算机科学技术研究所, 北京 100871;4.清华大学 软件学院, 北京 100084;5.湖南大学 信息科学与工程学院, 湖南 长沙 410082
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| 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. |
| 关键词: 知识图谱 数据管理 数据模型 查询语言 存储管理 查询操作 |
| DOI：10.13328/j.cnki.jos.005841 |
| 分类号:TP182 |
| 基金项目:国家自然科学基金（61572353）；天津市自然科学基金（17JCYBJC15400） |
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| Research on Knowledge Graph Data Management: A Survey |
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WANG Xin1,2, ZOU Lei3, WANG Chao-Kun4, PENG Peng5, FENG Zhi-Yong1,2
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1.College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;2.Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, China;3.Institute of Computer Science and Technology, Peking University, Beijing 100871, China;4.School of Software, Tsinghua University, Beijing 100084, China;5.College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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| 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. |
| Key words: knowledge graph data management data model query language storage management query operation |
