蛋白質交互作用的關係重要來源為PubMed醫學文件，透過文獻的閱讀可以了解目前生醫學界對蛋白質與蛋白質交互作用關係的相關研究，進而對生物路徑與蛋白質功能有更進一步的了解，然而文件的閱讀，耗廢非常大量的時間，使用電腦來輔助找尋蛋白質交互作用是較有效率的作法，在眾多的資訊萃取技術中，樣板模型的使用經過多年的研究，已有了一定的基礎，該方式不僅可靠，且較為貼近人類在文字理解的方法，在本論文中，以樣板模型的方式來進行蛋白質與蛋白質交互作用的分析，提出一個結合決策系統的方法來判斷蛋白質交互作用出現的關係，並且用來辨識蛋白質交互作用。
　　有別於一般系統使用人工方式訂立樣板規則，本研究使用自動化方式，建立蛋白質交互作用萃取樣板，並且提出幾個屬性來彌補樣板使用上的不足，經由實驗證明對於蛋白質驗證句的找出，具有極大的幫助。

　　The main source of protein-protein interactions is PubMed articles. By studying interactions from previous research, scholars all around the world can understand the states of current progress, and they can take advantage of these to expand pathway and protein functions. However the survey of documents is time-consuming and exhaustive. It is more efficient to use information extraction technology to filter superfluous information. To develop a robust and reliable system to mine protein-protein interactions, we need patterns for the recognition of natural language. In this paper, we propose a evidence providing system forprotein-protein interactions based on patterns and integrate decision model to extract PPI sentences from scientific literature.
　　Unlike other system depend on manual rules, we present a machine learning approach to construct patterns automatically and several attributes to assist to cooperate with pattern model. We also demonstrate that our system is able to provide protein interaction sentences well.

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