Combining rough set theory and instance selection in ontology mapping  

作　　者：Qian Pengfei Wang Yinglin Zhang Shensheng 钱鹏飞;Wang Yinglin;Zhang Shensheng(Deptartment of Computer Science and Engineering,Shanghai Jiaotong University,Shanghai 200240,P.R.China)

机构地区：[1]Deptartment of Computer Science and Engineering,Shanghai Jiaotong University,Shanghai 200240,P.R.China

出　　处：《High Technology Letters》2008年第3期258-265,共8页高技术通讯(英文版)

基　　金：the National High Technology Research and Development Program of China(No.2002AA411420);the National Key Basic Research and Development Program of China(No.2003CB316905);the National Natural Science Foundation of China(No.60374071)

摘　　要：This paper presents a novel ontology mapping approach based on rough set theory and instance selection.In this appoach the construction approach of a rough set-based inference instance base in which the instance selection(involving similarity distance,clustering set and redundancy degree)and discernibility matrix-based feature reduction are introduced respectively;and an ontology mapping approach based on multi-dimensional attribute value joint distribution is proposed.The core of this mapping aI overlapping of the inference instance space.Only valuable instances and important attributes can be selected into the ontology mapping based on the multi-dimensional attribute value joint distribution,so the sequently mapping efficiency is improved.The time complexity of the discernibility matrix-based method and the accuracy of the mapping approach are evaluated by an application example and a series of analyses and comparisons.

关 键 词：ontology mapping instance selection rough set feature reduction 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]