分布式本体调试信息的存储优化与增量更新  

Storage Optimization and Incremental Update of Ontology Debugging Information in Distributed Environment

作　　者：吴刚 闫智宣 邱煜晶 卢海强 李洋 WU Gang;YAN Zhixuan;QIU Yujing;LU Haiqiang;LI Yang(School of Computer Science and Engineering,Northeastern University,Shenyang 100619,China)

机构地区：[1]东北大学计算机科学与工程学院,辽宁沈阳100619

出　　处：《郑州大学学报（理学版）》2018年第4期1-7,共7页Journal of Zhengzhou University:Natural Science Edition

基　　金：国家自然科学基金项目(61370154;61332006)

摘　　要：为提高大规模本体信息的推理调试速度,提出了基于分布式数据库的RDFS和OWL pD*的推理调试方法,并优化存储模型和推理调试算法.此外,每次本体信息更新后,静态的本体推理和调试算法都需要完全地重新进行推理,因而提出了一种针对本体信息更新的增量算法,实现本体数据的动态更新.LUBM数据集的实验结果表明,基于分布式数据库是基于分布式文件的本体调试方法用时的15%～25%.在百万数量级三元组上,更新20%以内的数据,增量更新算法更有效.In order to improve the reasoning and debugging speed of ontology information, a reasoning and debugging method of RDFS and OWL pD＊ was proposed based on distributed database. And storage model and reasoning and debugging algorithm were optimized. In addition, once ontology information updated, static reasoning and debugging algorithms were required to completely reasoning, so an algorithm for incremental update ontology information was proposed, dynamically updating the ontology data. LUBM dataset results showed that ontology debugging method based on the distributed database was 15%～20% time-consuming compared to the distributed file system. Incremental update algorithm within 20% original dataset was more effective in million tuple.

关 键 词：分布式 本体调试 存储优化 增量更新 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]