摘要
给出通过计算向量之间的几何距离确定概念相似度的本体学习算法,并讨论了在实例空间映射到特征空间后距离测度的计算方法.最后利用3个仿真实验说明本文算法对相似度计算和本体映射的构建是有效的.
In recent years,with increasingly large amount of ontology data,ontology learning algorithm have raised more and more attention among experts.In these learning techniques,all the information of each ontology vertex(corresponding to a concept)is represented by a multi-dimensional vector.The core issue in ontology engineering applications is the similarity computing between concepts,thus it can be regarded as the distance calculating between the vectors.The smaller the distance,the larger similarity.In this paper,a new ontology learning algorithm is determined by means of geometric distance computation,thus obtain the similarity between the concepts.Furthermore,we discuss the distance computation method when the instance space is mapped into the feature space.Finally,three simulation experiments described to test the effective of our algorithm with respect to similarity measuring and ontology mapping.
作者
王燕
高炜
WANG Yan;GAO Wei(School of Information,Yunnan Normal University,Yunnan Kunming 650500,China;Department of Finance,Yunnan Normal University,Yunnan Kunming 650500,China)
出处
《西南师范大学学报（自然科学版）》
CAS
北大核心
2018年第1期40-46,共7页
Journal of Southwest China Normal University(Natural Science Edition)
基金
国家自然科学青年基金资助项目(11401519)
关键词
本体
相似度计算
本体映射
向量几何距离
ontology
similarity measuring
ontology mapping
vector geometry distance
