摘要
目前的RDF查询引擎存在忽略语义流数据量、查询延迟较高的缺陷。为了解决以上问题,将ECS索引和后向链式流推理相结合,加载新的RDF数据集并提取三元组特征集(Characteristic Set, CS)索引和扩展特征集(Extended Characteristic Set, ECS)索引,基于ECS索引后向推理,处理SPARQL查询并获取结果。对比结果表明,ECS索引和后向链式流推理相结合的方法可以明显提升RDF查询推理效率。
The current RDF query engine has the defects of ignoring the amount of semantic stream data and high query latency.In order to solve the above problems,this paper combined ECS index and backward chain stream reasoning,loaded new RDF data sets and extracted triple feature set(CS)index and extended feature set(ECS)index.Based on ECS index backward inference,we processed the SPARQL query and got the result.The comparison results show that the combination of ECS index and backward chain stream reasoning can significantly improve the efficiency of RDF query reasoning.
作者
韩裕镥
顾进广
李奇缘
Han Yulu;Gu Jinguang;Li Qiyuan(School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,Hubei,China;Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial,Wuhan 430065,Hubei,China;Big Data Science and Engineering Research Institute,Wuhan University of Science and Technology,Wuhan 430065,Hubei,China;Key Laboratory of Rich Media Knowledge Organization and Service of Digital Publishing Content,National Press and Publication Administration of the People's Republic of China,Wuhan 430065,Hubei,China)
出处
《计算机应用与软件》
北大核心
2023年第9期1-9,36,共10页
Computer Applications and Software
基金
国家社会科学基金重大项目(11&ZD189)
国家自然科学基金项目(61673304)
国家自然科学基金通用联合基金项目(U1836118)。
