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L/O/G/O 報告者:陳宜樺 報告日期: 2015/08/26 Research on Semantic Web Mining
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outline Introduction Web mining Semantic Web Semantic Web Mining Model 4 1 2 3
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Introduction(1/2) since the majority of Web data is unstructured, which lead to the traditional data mining results will be unsatisfactory Improve Web service levels and address the existing Web services which is supported by the lack of semantic problem
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Introduction(2/2) Semantic-based Web data mining =Semantic Web + Web mining
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Web mining Extract interested, useful patterns and implicit information from the WWW resources and behavior 方 法方 法 類 別
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Web Mining Types : Content Web Content Mining is a form of text mining The primary Web resource that is being mined is an individual page Web content mining can take advantage of the semi-structured nature of Web page text Web content mining can be used to detect co-occurrences of terms in texts
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Web Mining Types : Structure Usually operates on the hyperlink structure of Web pages The primary Web resource that is being mined is a set of pages, ranging from a single Web site to the Web as a whole Exploits the additional information that is (often implicitly) contained in the structure of hypertext.
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Web Mining Types : Usage The primary Web resource that is being mined is a record of the requests made by visitors to a Web site, most often collected in a Web server log. It is useful to combine Web usage mining with content and structure analysis in order to “make sense” of observed frequent paths and the pages on these paths It is also used for static site improvement by identifying navigational pattern of the user inside a Website.
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Semantic Web that embed machine-readable, on behalf of certain types of knowledge mark in the Web message
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Semantic Web Mining Model 建立初始本體 ( 取得核心概念 ) WSMO is a conceptual model for describing(SWS)s. It consists of four components, describing semantic aspects of (WS)s: 透過 Ontology Agent 處 理,整理新的資料數據 RDF clustering module 實現本體的資料來源 將 RDF database 提供給 Semantic web Mining module 再由 Ontology Agent 過濾 和處理群組,得出結果
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L/O/G/O Thank You!
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