大规模RDF三元组转换及存储工具比较研究
文献类型: 中文期刊
关键词: RDB2RDF;;RDF转换;;RDF存储;;大数据智能;;知识图谱
期刊名称:数字图书馆论坛
ISSN: 1673-2286
年卷期: 2020 年
页码:
收录情况: CSSCI(2021-2022版) ; 科技核心(2020版)
摘要: 富含语义知识的数据网络是实现大数据智能的基石。资源描述框架(Resource Description Framework,RDF)是用于描述网络资源的W3C标准。大规模转换、存储管理RDF三元组是构建关联数据网络或语义知识图谱,实现数据可查找、可访问、可交互、可再用的重要路径。本文选择国际主流的10种RDF三元组转换工具,以及6种广受欢迎的RDF存储系统,从技术原理、性能特点及应用场景等多个视角进行对比分析,并总结存在问题和不足。提出未来大规模RDF三元组数据转换与存储管理需要实现的目标是实现RDF抽取、转换和加载(ETL)的流程化和集成化,并重点支撑4类典型应用需求场景,包括从非RDF数据到RDF数据的转换,不同RDF数据格式之间的双向转换,RDF三元组在数据库之间的数据迁移,以及RDF数据的动态更新和进化管理。
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