摘要
为提高多个本体参与的大规模本体映射效率,提出基于概念分类的映射模型,将大映射问题转换为概念分类匹配问题,采用基于WordNet的概念粒度计算和语义相似度计算作为分类树的分类属性,通过快速排序算法实现概念分类,将输入概念分组。实验证明,提出的方法在保证映射准确性的同时减少了概念比较次数,降低了复杂度,方案具有可行性。
To improve the efficiency of ontology mapping with large scale multi-ontology,this paper proposed a method of multi-ontology mapping based on concept classification（MMBCC）.It transformed the problem of large scale ontology participation into concept classification problems.Granular computing and semantic similarity computation based on WordNet were property of the classification trees,realized the process of concept classification by quick sort algorithm.Experiments show that the proposed method guarantees the accuracy while reducing the mapping number of comparisons of concept,reducing the complexity,and the method is feasible.
出处
《计算机应用研究》
CSCD
北大核心
2011年第9期3335-3337,共3页
Application Research of Computers
基金
安徽省自然科学基金资助项目(11040606M133)
安徽省高校省级优秀青年人才基金资助项目(2011SQRL134)
安徽省高校省级自然科学研究资助项目(KJ2011B137)
关键词
多本体映射
分类树
概念分类
粒度
语义相似度
multi-ontology mapping
classification tree
concept classification
granularity
semantic similarity
