A new ontology optimization algorithm for similarity measuring and ontology mapping in multi-dividing setting

被引：0

作者：

School of Continuing Education, Southeast University, Nanjing, China ^{[1
]}

不详 ^{[2
]}

不详 ^{[3
]}

机构：

[1] School of Continuing Education, Southeast University, Nanjing

[2] School of Computer Science and Engineer, Southeast University, Nanjing

[3] School of Information Science and Technology, Yunnan Normal University, Kunming

来源：

J. Comput. Inf. Syst. | / 9卷 / 3297-3305期

基金：

中国国家自然科学基金;

关键词：

Gradient computation; Multi-dividing; Ontology; Ontology mapping; Similarity measure;

D O I：

10.12733/jcis14244

中图分类号：

学科分类号：

摘要：

Ontology, as a model of knowledge store and represent, has widely used in biology, chemistry, pharmaceutics and education science. In this paper, by virtue of gradient computation and iterative calculation, we present a new optimization model for ontology similarity measure and ontology mapping in multi-dividing setting. The idea to design the model heavily depended on AUC criterion of multi-dividing ontology setting, and the model can deal with the high-dimension data situation. At last, two simulation experiments to show that our new model has higher precision ratio on biology ontology and software ontology for similarity measuring and ontology mapping applications. ©, 2015, Binary Information Press. All right reserved.

引用

页码：3297 / 3305

页数：8

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