概念模型(ontology)的建構侷限了概念模型的應用與發展，傳統上概念模型的建構是以人工去定義領域概念與概念間的關係，然而人工建構概念模型是費時，費力且因人的主觀意識使得結果極具爭議性，造成概念模型無法普及應用。因此，本論文結合自然語言處理及文件探勘技術由非結構化的領域文集自動建構領域概念模型，自動建構架構包含三個主要部分: 領域專有名詞的萃取、概念探勘及概念階層關係的發掘、非階層語意關係的發掘。最後並提出以概念模型為知識庫的智慧型資訊擷取系統，有效應用此機器自動建構的概念模型。

　　An important debate of modern ontology science and technique is how to automatically construct an ontology from the bush of raw data. The traditional methods on constructing domain ontology are manually defining a set of domain terminologies and concepts, and then deciding their relation. However, this task can be lengthy, costly, and controversial because people can have different points of view about the same concept. In this thesis, we develop a new ontology learning method that automatically analyzing and constructing the ontology from domain text. The proposed method utilized several techniques, such as natural language processing and text mining method etc. It comprises three basic stages: the domain terminology extraction, the concepts exploration & concept hierarchy arrangement, and the non-taxonomy relations discovering. We further propose ontology-based intelligent information retrieval system that utilized the resulted ontology to summarize the user query and categorize the retrieval results.

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