網頁資料擷取在許多智慧商業任務中是一個關鍵元件，像是資料的轉換、交換、分析和解釋。已經有許多人工、監督式或非監督式的Wrapper induction方法被提出 。但是大多數的研究都專注在資料擷取的成效，並沒有專注在擷取的效率。在這篇論文中，我們顯示出非監督式網頁資料擷取的Wrapper生成是和監督式的Wrapper induction同等重要的，因為已經生成的Wrapper可以不需要複雜的分析並更有效率地完成任務，因此，我們將非監督式網頁擷取視為一個Oracle Machine來生成標記的訓練資料並採用兩種方法來生成Wrapper：Schema引導的Finite-State Machine (FSM)和資料驅動的機器學習方法。實驗結果顯示FSM生成的Wrapper可以在較少量的訓練資料中便達到好的成效，而機器學習類的方法則是在測試時更有效率但需要較多的訓練資料來達到同等的成效。此外，FSM生成的Wrapper可以當作是機器學習類方法的Filter來達到減少資料量並改善學習曲線的效果。;Web data extraction is a key component for many business intelligence tasks, such as data transformation, exchange, analysis, and interpretation. Many approaches have been proposed for wrapper induction, either manual, supervised or unsupervised. However, most research focuses on extraction effectiveness. Not much attention has been paid to extraction efficiency. In this thesis, we argue that wrapper generation for unsupervised web data extraction is as important as supervised wrapper induction because the generated wrappers could work more efficiently without sophisticated analysis. Therefore, we can treat unsupervised data extraction as an oracle machine to generate annotated training examples and consider two methods of wrapper generation: schema-guided finite-state machine (FSM) approaches and data-driven machine learning (ML) approaches. The experimental result shows that the FSM wrapper can perform well even with fewer training data, while the ML-based models are more efficient during testing but require more training pages to achieve the same effectiveness. Furthermore, FSM wrappers can work as a filter to reduce the number of training pages and advance the learning curve for ML-based wrappers.
