知識本體(Ontology)是一種知識表達法，主要是建構出人類的知識概念，及概念間的關連，其特性乃是：正規，共享，重用。這些特性使得知識本體間可以直接經由電腦達到自動化的溝通而不需要人工介入---這是知識本體應用最終目標。
　　然而各組織在建置自己的知識本體之前，並無事先溝通建置標準。因此可以說，沒有重複的知識本體。當跨組織知識分享的需要產生時，所帶來的就是不同規格標準的知識本體整合問題。從2000年有許多學者提出不同知識本體間的比對方法(ontology mapping )。其中領域語彙典，是一項重要輔助資源，它能解決同義異詞的問題，找出語意上正確的概念對應關係。但缺點是語彙典建置需要大量人工、時間，更新慢，使得一些新的用詞，或新興領域用語，無法即時被收錄。因此本體比對若遇到新詞，常常推論錯誤。
　　本研究為了解決知識本體在做比對時時輔助資訊(information sparseness)不足的問題。我們利用網際網路的資訊，它不止量大，更是常常有新的資訊加入，不會有找不到新詞的疑慮。然而，雜訊多一直是網頁資訊的缺點。為此，許多過濾的機制便因應而生，其中一個顯著有效的，並可應用於字詞關係推論的方法，就是”語意式網路探勘技術”。它藉由找尋一些特定結構的句子，來判斷字詞間的關連。我們利用這種概念，並加入網路位址，以及連結的分析，希望從網頁找出更豐富的資訊，來協助知識本體整合。這樣的系統，其背後的文件庫乃是日新月異且龐大的網際網路資源，這樣的方法可擺脫語彙典的限制，使知識本體整合更具時效性。

　　Ontology is a kind of knowledge representation model. It can represent the concepts of humans and the relationship between concepts. Owing to its characters which are formal, explicit, and sharing, the computers can communicate with each other through ontologies automatically.
　　However, different organizations construct their own ontologies to use by themselves. It brought the situation that many ontologies appears but stand on different standards. When Knowledge sharing requirement between organizations arises, the integration problem between ontologies with variant standards happens. Since 2000, many researches attempt to deal with the ontology integration problem. In their methods, the thesauri are main auxiliary information resources. The thesauri are used to detect synonym, hypernym, and hyponym in the mapping process, and increase the mapping precision. But the thesauri construction are time-consuming and human power-consuming. It causes two problems. First, for the new domain, it does not have a thesaurus. And for the new terms, they are not put in to the thesaurus in time. Owing to the bottleneck, the ontology mapping applications above are restricted to some domains.
　　To solve the problem of auxiliary information lacking, we utilize web as our resources. For find the precise concepts relations on web, we use sites structure analysis and linguistic pattern mining to find some concepts relations cues. Then we combine both cues to extract the hierarchy knowledge hidden in the site. The hierarchy knowledge can be used to support ontology mapping process to improve mapping precision.

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