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| 一种新的本体映射发现方法SME | |
| 引用本文： | 夏红科,郑雪峰,胡祥.一种新的本体映射发现方法SME[J].计算机科学,2010,37(6):233-236. |
| 作者姓名： | 夏红科 郑雪峰 胡祥 |
| 作者单位： | 1. 北京科技大学信息工程学院,北京,100083;北京信息科技大学计算机学院,北京,100192 2. 北京科技大学信息工程学院,北京,100083 3. 北京邮电大学计算机科学与技术学院,北京,100876;华北电力大学计算机科学与技术学院,北京,102206 |
| 基金项目： | 国家高技术研究发展(863)计划，北京信息科技大学校基金 |
| 摘 要： | 本体映射是基于本体的语义查询与集成的基础.本体映射发现的任务是从源本体和目标本体的相似度中发现本体映射,它是本体映射的关键.将本体映射发现问题看成是集合覆盖问题,提出一种基于集合覆盖的本体映射发现方法SME(SCM-hased Mapping Extraction),该方法在训练阶段找到最大程度覆盖训练数据的属性集,在测试阶段利用这些属性集在测试数据上对应属性值的交操作来发现映射.实验证明该方法具有较好的综合性能. |
| 关 键 词： | 映射发现 集合覆盖 数据依赖球 属性空间 |
| 收稿时间： | 2009/7/28 0:00:00 |
| 修稿时间： | 2009/10/19 0:00:00 |
Novel Approach of Ontology Mapping Extraction SME |
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| XIA Hong-ke,ZHENG Xue-feng,HU Xiang.Novel Approach of Ontology Mapping Extraction SME[J].Computer Science,2010,37(6):233-236. | |
| Authors: | XIA Hong-ke ZHENG Xue-feng HU Xiang |
| Affiliation: | (Information Engineering Institute,University oI Science&Tech. Beijing,Beijing 100083,China), (Computer School,Beijing Info. Science & Tech. University,Beijing 100192,China),(School of Computer Science &Tech. ,Beijing University of Posts and Telecommunications,Beijing 100876,China),(Computer Science&Tech. Dept. ,North China Electric Power University, Beijing 102206,China) |
| Abstract: | Ontology mapping is the foundation of semantic query and semantic integration based on ontology. As the crucial point of ontology mapping, the task of mapping extraction is to find whether there exists the ontology mapping among the similarities between source ontology and target ontology. In this paper the problem of mapping extraction was regarded as the problem of set covering, and a novel ontology mapping extraction algorithm based on set covering SME (SCM-based Mapping Extraction) was proposed, by which the property set that covers the training data set in maximum degree was searched during training stage, and the mapping extraction was carried out by means of the conjunction of the properties of property set in testing data set during testing stage. Experimental evaluations show that this method has better comprehensive performances compared with other algorithms. |
| Keywords: | Mapping extraction Set covering Data-dependent ball Property spaces |
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