在資訊暴增的今日，人們常容易迷失在大量的訊息之中，雖然資訊檢索的發展減輕了負擔，但傳統的方法卻也漸漸的無法滿足使用者。本論文嘗試跳脫傳統利用統計文件關鍵字，抽象化表現文件概念的方法；改經由語意理解的角度，去推論文章的內容，以期望帶給更符合使用者需求的搜尋結果。在論文中，首先我們提出一套物件導向式的模型，架構出領域實體論，作為語意推論的知識庫。接著我們利用文法規則精確剖析文章，擷取出中心語意，再搭配建構在物件導向式實體論的模糊推論引擎，逐層解析內容，並建構出具有語法性質的索引結構，最後搭配Extended Boolean Model，達到個人化的檢索機制。關於模糊推論引擎的效能調校，我們引用基因演算法搭配專家的建構機制，以期增強系統的強韌度。此外關於中文文法的處理，我們採用中研院提供的CKIP工具做詞性標注等額外的輔助工作。

Nowadays, we can retrieve information from search engine, but traditional search engine can’t perform the search with high quality and low quantity of information. That’s because of traditional search engine mainly use pure keyword and some statistic data to process the similarity between query words and documents. In the thesis, we first propose an infrastructure of object-oriented ontology to be the knowledge base of fuzzy inference model. Then we apply Chinese grammar to do syntax processing for separating different parts of sentences, and we put kernel of sentences to gradually infer the resulted instance with domain ontology. We collect various linguistic messages in documents, such as passive, negative voice and semantic degree to store in index at this stage. At last we apply Extended Boolean Model to be the personalized ranking mechanism. On the other hand, we apply Genetic Algorithm on tuning the parameters of TSK model to make our system more robust. Besides, we take the CKIP as the part-of-speech tagging tool, and it is the base of Chinese grammar analysis.

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