高技术通讯（英文版）2008，Vol.14Issue(3)：258-265,8.
Combining rough set theory and instance selection in ontology mapping
Combining rough set theory and instance selection in ontology mapping
摘要
Abstract
This paper presents a novel ontology mapping approach based on rough set theory and instance selection.In this approach 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 approach is the 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 sequenfly 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 reductionKey words
ontology mapping/instance selection/rough set/feature reduction分类
数理科学引用本文复制引用
Qian Pengfei ,Wang Yinglin,Zhang Shensheng..Combining rough set theory and instance selection in ontology mapping[J].高技术通讯（英文版）,2008,14(3):258-265,8.基金项目
Supported by the National High Technology Research and Development Program of China (No.2002AA411420).the National Key Basic Research and Development Program of China (No.2003 CB316905) and the National Natural Science Foundation of China (No.60374071). （No.2002AA411420）
