Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment

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• 作者：Mye Sohn (1)
  Sunghwan Jeong (1)
  Hyun Jung Lee (2)
  
• 关键词：Case ; based context ontology ; Personalized service ; Fuzzy membership function adaptation ; Fuzzy set theory
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2014
• 出版时间：September 2014
• 年：2014
• 卷：18
• 期：9
• 页码：1715-1728
• 全文大小：1,605 KB
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• 作者单位：Mye Sohn (1)
  Sunghwan Jeong (1)
  Hyun Jung Lee (2)
  
  1. Department of Industrial Engineering, Sungkyunkwan University, 300, Chunchun-dong, Jangan-gu, Suwon, Gyeonggi-do?, 440-746, Korea
  2. Yonsei Institute of Convergence Technology, School of Integrated Technology, Yonsei University, 162-1, Songdo-dong, Yeonsu-gu, Inchon, Korea
  
• ISSN：1433-7479

文摘

To provide context-based personalized services utilizing smart appliances in a smart home environment, we propose a framework for PersonAlized Service disCovery Using FuZZY-based CBR and Context Ontology (PASCUZZY). Basically, the PASCUZZY framework is implemented on case-based context ontology. To generate and manage the case instances on the case-based context ontology, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment. The context is transposed to linguistic-type context instances on the context ontology. In addition, to formalize and manage the context and services as multi-attributed data, the context ontology was developed reflecting the structure of cases borrowed from case-based reasoning. Furthermore, we propose adaptation methods to adjust the generic fuzzy membership functions depending on the inhabitants-context. It is performed by modifying the values of the membership number and/or modifying the numbers of the linguistic terms that are based on the inhabitants-context to affect the membership numbers. The adapted membership functions return the personalized degree of memberships depending on the specialized context of a specific fuzzy variable. Inevitably, the number of cases on the case-based context ontology will be increased from time to time. We apply Ward’s method not only to reduce the search effort via a hierarchical clustering on the case-based context ontology but also to find the most similar service as a solution to the new context. To verify the superiority of the PASCUZZY framework, we perform two kinds of evaluations. First, we evaluate the effectiveness of the adaptation of the fuzzy membership functions. Second, we verify the effectiveness of the application of a clustering method to the case instances of the case-based context ontology to identify the most similar service. Results of the experiment verified the effectiveness and superiority of the PASCUZZY framework.