網際網路的盛行，使得世界各地的人們隨時都能存取大量資訊，然而一般獲取資訊的方法均以特定關鍵字為基礎，忽略其相關的意涵與潛在的脈絡關係，使資訊的探索只侷限於特定字義的範圍。所以遂有本體論(Ontology)的產生，其目的在建立一個正規、共享、具重用性的知識表現架構，藉知識個體間的語意關聯，強化知識主體的完整性與溝通性。
　　近年來，隨著本體論概念廣被學界與業界應用，亦開始呈現類似網際網路資訊的問題，即同一領域，甚或是企業之中，均存有多個不同本體論同時運作，資訊與系統的整合已成為迫切要務之一。於是學者們紛紛提出各種本體論的整合方法，範疇遍及系統、架構與流程等構面。在這些方法之中，有以單一本體論為整合結果，亦有建立由上而下完整的整合流程，然而這些方式與架構仍有著一些缺點需要改進，諸如原有資源喪失獨立維護性，或是實際執行過度依賴協商妥協，導致整合效益不彰。
　　因此，為了避免上述整合本體論可能產生的後遺症，本研究參考過去相關文獻， 擬改良Ontology Clustering的整合架構，融合語法語意相似度測量方法，增加概念比較時的準確性以避免資訊的流失，並提供本地端導向的搜尋方式，期能使各社群依其興趣和觀點獲得所需的知識，擴大知識共享的效益；亦計畫於特定領域中，此本體論整合架構能成為社群共同發展領域知識的管道之一。

　In recent years, with ontology prevailing over educational circles and industry application, like internet, one of the interesting issues is information highway is congested with considerable duplicate information. Even in enterprises or the same field, lots of difference ontology are operated simultaneously, the integration of information and system has already become one of the urgent important tasks. Then many researchers propose their methods to solve these problems form different viewpoint such as system, structure and procedure etc. In these methods, some prefer a single ontology as the result of combination, and others also construct the framework by a top-bottom procedure. Some shortcomings need improvement for these ways, nevertheless, for example, original resources are lost the autonomy, or operations depend on undue compromise actually. All of these might reduce benefit of integration.
　Therefore, in order to avoid the side effect above-mentioned, We consults relevant researches of the past to improve the 「Ontology Clustering」 with the syntactic and semantic similarity measures, moreover, the main purpose is to ensure the accuracy in concept integration and avoid the information loss. Then, we design a local-view oriented searching method which can help communities obtain necessary knowledge in accordance with his interest and view to expand benefit of knowledge sharing.

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