Combining rough set theory and instance selection in ontology mapping

This paper presents a novel ontology mapping approach based on rough set theory and instance selec-tion.In this approach the construction approach of a rough set-based inference instance base in which theinstance selection(involving similarity distance,clustering set and redundancy degree)and discernibilitymatrix-based feature reduction are introduced respectively;and an ontology mapping approach based onmulti-dimensional attribute value joint distribution is proposed.The core of this mapping approach is theoverlapping of the inference instance space.Only valuable instances and important attributes can be se-lected into the ontology mapping based on the multi-dimensional attribute value joint distribution,so thesequently mapping efficiency is improved.The time complexity of the discernibility matrix-based methodand the accuracy of the mapping approach are evaluated by an application example and a series of analy-ses and comparisons.