實體論(Ontology)在許多的資訊系統及語意網(Semantic Web)中越來越重要，但建構實體論往往需要耗費大量的時間，且建構完成後維護實體論對知識管理者來說也是費時的工作。本論文中，我們提出基於插曲探勘(Episode Mining)與模糊推論之中文文件建構實體論方法；此外，我們以物件導向模式來表示實體論，並提出一個四層式物件導向實體論結構，用此種結構來建造領域知識。在我們提出的方法中包含實體論建構及實體論學習兩部分，在實體論建構方面，採用自組織映射圖網路來找出實體論中的概念(Concept)與實體(Instance)，利用插曲探勘及自然語言處理來找出概念中靜態的屬性(Attribute)、動態的行為(Operation)與概念間的關連關係(Association)；在實體論學習方面，利用三層的平行模糊推論機制來得到新的實體，並擷取出新的資訊來更新原有的領域實體論；經由實驗驗證，此方法能有效的協助知識管理者建構及維護中文新聞之實體論。

Ontology is increasingly important in many information systems and Semantic Web. The problem of it is that the construction of ontology is a time-consuming job and ontology engineers have to spend much time to maintain it. In this thesis, we propose an episode-based fuzzy inference mechanism to extract domain ontology from unstructured Chinese news documents. In addition, we propose a four-layer object-oriented ontology to structure domain knowledge. This approach contains domain ontology construction and domain ontology learning task. The Self-Organization Map (SOM) algorithm is adopted for concept clustering and taxonomic relation defined. Moreover, the attributes, operations of concepts and associations between concepts can be extracted based on episodes and morphological analysis. The three-layer parallel fuzzy inference mechanism is further applied to obtain new instances for ontology learning. Besides, new information will be extracted to update the domain ontology. The experimental results show that our approach can effectively assist ontology engineers to construct and maintain the Chinese news domain ontology.

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