Leave-two-out stability of ontology learning algorithm |
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| Institution: | 1. School of Computer Science and Engineer, Southeast University, Nanjing 210096, China;2. School of Continuing Education, Southeast University, Nanjing 210096, China;3. School of Computer Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China;4. School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China;1. Bussiness School, Sichuan University, Chengdu, 610064, P.R. China;2. College of Management Science, Chengdu University of Technology, Chengdu, 610059, P.R. China;1. School Of Internet Of Things Engineering, Jiangnan University, Wuxi 214122, China;2. Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;1. School of Economics & Management, Yanshan University, Qinhuangdao 066004, PR China;2. Gannan Normal University, Ganzhou 341000, PR China;3. Research Center of Hubei Logistics development, Hubei University of Economics, Wuhan 430205, PR China;1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;2. Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;3. School of Electrical, Electronic and Computer Engineering, Lancaster University, Bailrigg, Lancaster, LA1 4YW, United Kingdom;1. International School of Education, Xuchang University, Xuchang, Henan 461000, PR China;2. College of Information Engineering, Xuchang University, Xuchang, Henan 461000, PR China;3. School of computer science and engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, PR China |
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| Abstract: | Ontology is a semantic analysis and calculation model, which has been applied to many subjects. Ontology similarity calculation and ontology mapping are employed as machine learning approaches. The purpose of this paper is to study the leave-two-out stability of ontology learning algorithm. Several leave-two-out stabilities are defined in ontology learning setting and the relationship among these stabilities are presented. Furthermore, the results manifested reveal that leave-two-out stability is a sufficient and necessary condition for ontology learning algorithm. |
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