我們從自然語言領域中借用Thesaurus模型作為字彙關聯的基礎，陸續加入Folksonomy概念、Social Network Service指標的蒐集以及domain-specific ontology來建構Tag-Thesaurus模型，用來解決使用一般tagging system資源彙整能力不足的問題。首先我們對將要實驗的領域選取初始字彙，並利用這些字彙建構Tag-Thesaurus模型。接著將預先準備的這些字彙釋放到社會網路服務平台的tagging system中，透過社會網路服務平台中的tagging system來蒐集使用者對於資源的平面分類資訊，利用這些資訊來對Tag-Thesaurus模型持續地擴充。透過這樣的Tag-Thesaurus模型，我們將可以獲得較佳的資源彙整。domain-specific ontology的加入將可以強化由上而下的資源彙整。而Social Network Service當中的其他資訊，如FOAF[16]或是個人的偏好等，將可以提昇個人化資源彙整的能力。這樣的結合方式不僅是ontology應用的示範，我們更希望透過這樣的混合式模型，使得Web 2.0這樣子廣泛蒐集眾人智慧的概念能夠成為跨入語意網的橋樑。 / We aggregate various resources through the Tag-Thesaurus Model. There are three parts in Tag-Thesaurus model, the Folksonomy formal model, indices collection on Social Network Service, and lightweight domain-specific ontology. The Folksnomy model reconstruct relationships between tags, and we can aggregate resources by tags. The indices collection on Social Network Service help us to decide which resource are more important. Finally, the lightweight domain-specific ontology provide the standard interface to describe the relationships between tags.
| Identifer | oai:union.ndltd.org:CHENGCHI/G0094753024 |
| Creators | 宋昆銘 |
| Publisher | 國立政治大學 |
| Source Sets | National Chengchi University Libraries |
| Language | 中文 |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
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