隨著生物醫學文獻的快速增加，資訊萃取（Information Extraction）在生物資訊學（Bioinformatics）中正迅速的變成一個不可缺少的技術。而其中以樣式（Pattern）比對為基礎的資訊萃取方法更能夠快速、準確的從文件中萃取出有意義的資訊及知識。
　　在本論文中，我們提出一個以項目集樹（Item-Set Tree）為基礎之樣式探勘（Pattern Mining）的方法，來取代一般以樣式為基礎的資訊萃取方法中所需要的、耗時費力的手動訂立樣式的過程，並以此發展出一套從生物醫學文獻中自動辨識蛋白質相關資訊—生物程序（Biological Process）、細胞成分（Cellular Component）、分子功能（Molecular Function）—的系統，並且能產生相對應的、以基因本體論（Gene Ontology；GO）為基礎的註解。
　　本論文以樣式探勘方法來尋找樣式之基本觀念是，此方法可以將描述方式類似的句子中，主要特徵的部分篩選出來；換言之，將次要的部分濾除，例如只為加強語氣的字眼，再利用這些特徵從文件中找出描述蛋白質功能的句子。但是一般探勘方法卻會有忽略低頻率特徵的的缺點。針對此點，本論文使用以項目集樹為基礎的探勘方法克服之，對於低頻卻可做為特徵的項目，此方法依然可以正確地篩選出來。

　　With the accelerative availability of biological literatures, research on information extraction is rapidly becoming an essential component of various bioinformatics applications. The pattern-based information extraction approach can extract information and knowledge from documents quickly and accurately.
　　In this thesis, we propose a pattern mining methodology based on the item-set tree to substitute the time-consuming manual pattern establishment by experts in pattern-based information extraction approach. We implement an automatic function annotation system, which can extract gene and/or protein information about biological process, cellular component, and molecular function, and produce Gene Ontology-based annotations.
　　The proposed method can cluster similar sentences and extract the main descriptive components. In general data mining approach, infrequent patterns will be ignored, but the proposed method can overcome this drawback.

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