A graph derivation based approach for measuring and comparing structural semantics of ontologies
文献类型：期刊论文
| 作者 | Ma, Yinglong (1) ; Liu, Ling (2) ; Lu, Ke (3) ; Jin, Beihong (4) ; Liu, Xiangjie (1) |
| 刊名 | IEEE Transactions on Knowledge and Data Engineering
 |
| 出版日期 | 2014 |
| 卷号 | 26期号:5页码:1039-1052 |
| 关键词 | Ontology ontology measures ontology comparison ontology reuse |
| ISSN号 | 10414347 |
| 中文摘要 | Ontology reuse offers great benefits by measuring and comparing ontologies. However, the state of art approaches for measuring ontologies neglects the problems of both the polymorphism of ontology representation and the addition of implicit semantic knowledge. One way to tackle these problems is to devise a mechanism for ontology measurement that is stable, the basic criteria for automatic measurement. In this paper, we present a graph derivation representation based approach (GDR) for stable semantic measurement, which captures structural semantics of ontologies and addresses those problems that cause unstable measurement of ontologies. This paper makes three original contributions. First, we introduce and define the concept of semantic measurement and the concept of stable measurement. We present the GDR based approach, a three-phase process to transform an ontology to its GDR. Second, we formally analyze important properties of GDRs based on which stable semantic measurement and comparison can be achieved successfully. Third but not the least, we compare our GDR based approach with existing graph based methods using a dozen real world exemplar ontologies. Our experimental comparison is conducted based on nine ontology measurement entities and distance metric, which stably compares the similarity of two ontologies in terms of their GDRs. Copyright © 2013 IEEE. |
| 英文摘要 | Ontology reuse offers great benefits by measuring and comparing ontologies. However, the state of art approaches for measuring ontologies neglects the problems of both the polymorphism of ontology representation and the addition of implicit semantic knowledge. One way to tackle these problems is to devise a mechanism for ontology measurement that is stable, the basic criteria for automatic measurement. In this paper, we present a graph derivation representation based approach (GDR) for stable semantic measurement, which captures structural semantics of ontologies and addresses those problems that cause unstable measurement of ontologies. This paper makes three original contributions. First, we introduce and define the concept of semantic measurement and the concept of stable measurement. We present the GDR based approach, a three-phase process to transform an ontology to its GDR. Second, we formally analyze important properties of GDRs based on which stable semantic measurement and comparison can be achieved successfully. Third but not the least, we compare our GDR based approach with existing graph based methods using a dozen real world exemplar ontologies. Our experimental comparison is conducted based on nine ontology measurement entities and distance metric, which stably compares the similarity of two ontologies in terms of their GDRs. Copyright © 2013 IEEE. |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000337965900001 |
| 公开日期 | 2014-12-16 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16698]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Ma, Yinglong ,Liu, Ling ,Lu, Ke ,et al. A graph derivation based approach for measuring and comparing structural semantics of ontologies[J]. IEEE Transactions on Knowledge and Data Engineering,2014,26(5):1039-1052. |
| APA | Ma, Yinglong ,Liu, Ling ,Lu, Ke ,Jin, Beihong ,&Liu, Xiangjie .(2014).A graph derivation based approach for measuring and comparing structural semantics of ontologies.IEEE Transactions on Knowledge and Data Engineering,26(5),1039-1052. |
| MLA | Ma, Yinglong ,et al."A graph derivation based approach for measuring and comparing structural semantics of ontologies".IEEE Transactions on Knowledge and Data Engineering 26.5(2014):1039-1052. |
入库方式： OAI收割
来源：软件研究所
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