DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Ko, In-Young | - |
dc.contributor.advisor | 고인영 | - |
dc.contributor.author | Jeon, Beom-Jun | - |
dc.contributor.author | 전범준 | - |
dc.date.accessioned | 2011-12-28T03:00:41Z | - |
dc.date.available | 2011-12-28T03:00:41Z | - |
dc.date.issued | 2007 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=392852&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/54872 | - |
dc.description | 학위논문(석사) - 한국정보통신대학교 : 공학부, 2007.8, [ viii, 58 p. ] | - |
dc.description.abstract | Ontology is widely used for representing and sharing bio information. However, ontology alone can not be used to represent non-deterministic relationship among E-health information such as symptoms and diseases. Although Bayesian network (BN) is one of the popular methods to represent and reason with probabilistic knowledge for E-health applications, it is normally a complex task to construct a BN model by collecting and analyzing domain knowledge. Therefore, we propose a semi-automatic way of constructing BN models from ontology for diagnosing diseases in the E-health applications. Our method allows users to select abstract levels to specify important terms for a specific application and to determine complexity. Our approach automatically generates nodes in a BN model out of E-health ontology, and allows users to easily establish links among nodes based on a meta model that represents cause-and-effect relationships among ontologies. In addition, to reduce the complexity and to improve the robustness of a BN model, we use an intermediate ontology that makes an application-specific abstraction of E-health ontology. We also present a diagnosis process using the E-health ontologies and constructed BN models. We tested our approach with an obesity management application. For diagnosing obesity, we set a scenario about service providing on the web portal, constructed E-health ontology, built BN model out of the ontology, and implemented an obesity-diagnosing system by adapting the diagnosis process. We also evaluate the effectiveness of our approach by comparing it with the methodologies used in related works of BN construction from ontology. | eng |
dc.language | eng | - |
dc.publisher | 한국정보통신대학교 | - |
dc.subject | Semi-automatic Bayesian network construction | - |
dc.subject | E-health ontology | - |
dc.subject | E-health applications | - |
dc.subject | E-health 응용 | - |
dc.subject | 반자동화 베이지안 네트워크 구축 | - |
dc.subject | E-health 온톨로지 | - |
dc.title | Semi-automatic bayesian network construction from ontology for e-health applications | - |
dc.title.alternative | E-health 응용을 위한 온톨로지로부터의 반자동화된 베이지안 네트워크의 구축 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 392852/225023 | - |
dc.description.department | 한국정보통신대학교 : 공학부, | - |
dc.identifier.uid | 020054670 | - |
dc.contributor.localauthor | Ko, In-Young | - |
dc.contributor.localauthor | 고인영 | - |
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