隨著資訊科學的進步，大量生物醫學的研究成果被記載在文獻中。針對於蛋白質交互作用方面，現今雖有相關的資料庫，但是所能提供的資料有限，並且是以人工的方式從生物醫學文獻中萃取及驗證蛋白質交互作用的資訊，過程是費時及昂貴的。我們在本研究中提出了自動化萃取蛋白質交互作用的過程，使用階層式樣板的比對，來找出對於蛋白質交互作用描述之句子，並且針對於這些句子建立彼此的鏈結關係，以修改的PageRank演算法來對其作排序，找出蛋白質的交互作用關係。在本論文研究中，我們實作一個自動化的文件探勘系統，除了驗證在KEGG反應路徑上所記載的重要蛋白質關係之外，並且找出了大量可能的潛在蛋白質關係。與過去存在類似的系統或資料庫相比，本系統提供蛋白質交互作用驗證句、蛋白質作用關係等較為豐富且大量的資訊。在最後的實驗結果也顯示，本系統對於蛋白質交互作用資訊的找出有相當不錯的能力。

Along with the improvement of information and computational techniques, increasing number of biomedical researches and literatures have been reported at the public databases such as PubMed. As for identifying protein-protein interactions, there have been some related databases manually evidence and extract interaction data from biomedical literatures. But they offer limited information and the process is time-consuming and expensive in labor power. To enhance the protein-protein interaction extraction process, we implemented an automated framework that combing hierarchical template-based sentence matching and PageRank-based sentence ranking approaches. Using this framework, we extract the interaction evidence sentences and their interaction relations. In this research, we implement a text-mining system to identify many important relations in KEGG pathway databases and discover a great number of novel relations that could potentially extend the existing protein interactions and pathways databases.

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