有越來越多的人使用串聯樣式表來管理他們的網頁，因為串聯樣式表對於排版是方便且容易。但在網頁資料擷取上，串聯樣式表網頁通常會造成網頁結構模糊，這使得利用比對網頁結構來擷取資料的系統發生誤判。為了解決這個問題，本篇論文描述了一個系統能利用串聯樣式表網頁上的特質來擷取資料區塊。系統包含了三個部份，分別是樹生成、資訊熵計算模型和區塊識別。首先，在樹生成的步驟中，系統利用網頁節點的視覺資訊和標籤名稱去轉換網頁成為一棵視覺樹。視覺樹描述資料在視覺上是如何呈現在瀏覽器上。在資訊熵計算模型的步驟中，視覺樹中的每一個節點會依照特定的模型去計算其資訊熵，接著這些資訊熵在區塊識別的步驟中是被拿來作為標定區塊形態的準則。資訊熵非常適合用來測量一區塊內的資訊量；如果某區塊含有各式各樣的內容，則該區塊的資訊熵就會相對的高。由實驗結果可以得知我們的系統比其他系統在擷取容器區塊上是有較好的效能，節點特徵與視覺樹也的確能提升在串聯樣式表網頁中區塊擷取的效果。

More and More people use Cascading Style Sheets (CSS) to manage their Web pages, because CSS is easy and convenient to typesetting. However, CSS makes a Web pages displayed in an ambiguous structure. The data extraction systems based on Web page structure matching could generate more mistaken judgments. Furthermore, they only identify blocks with similar structures. Some systems use specific HTML tags such as TABLE, TR, TD and P, to partition Web pages, but the proportion of the tags is generally less than DIV tags in CSS Web pages. In this paper, for solving the limitations, we present a system that applies properties of CSS Web page to extract data block. The system comprises three modules: Visual Tree Generation (VTG), Entropy Evaluation Model (EEM) and Block Identification (BI). Web pages are first converted into tree objects in the VTG module. The module transforms DOM trees into visual trees by using the visual information and HTML tag name of nodes to modify tree structure. The proposed visual tree presents the arrangement of data displayed on Web Browser, which meet the visual intention for evaluating informative blocks. If a block consists of diverse content, the block entropy will be relatively high. Thus entropy is proper to measure the information content of blocks for distinguishing presentation blocks from others. In the EEM module, the entropy attributes of each node in a visual tree is calculated. These attributes are used to identify block types by the BI module which comprises the block marking and block Refining. In the experiment, the result shows the node attributes and the visual tree are useful to extract blocks on CSS Web pages. Our system also outperforms with other systems on container block extraction.

中文摘要 III
ABSTRACT IV
CONTENT V
FIGURE LISTING VII
TABLE LISTING IX
1. INTRODUCTION 1
2. RELATED WORK 5
2.1 DOM-BASED PAGE SEGMENTATION 5
2.2 VISION-BASED PAGE SEGMENTATION 6
2.3 HYBRID APPROACH 7
2.4 OTHER 8
2.5 IB APPLICATION 8
3. THE PROPERTIES OF CSS WEB PAGE 11
3.1 CSS SELECTOR ENTROPY 11
3.2 LAYER CONTAINMENT 12
3.2.1 Structural Containment 12
3.2.2 Visual Containment 13
3.3 CSS DEFINITION 14
4. SYSTEM ARCHITECTURE 16
4.1 VISUAL TREE GENERATION 16
4.1.1 DOM Parser 17
4.1.2 Tree Constructer 17
4.1.3 Tag Filter 18
4.1.4 Visual Information Module 19
4.2 ENTROPY EVALUATION MODEL 20
4.2.1 Attribute Entropy 20
4.2.2 Node Attributes 21
4.2.3 Child Entropy Evaluation 21
4.2.4 Path Entropy Evaluation 23
4.2.5 Partial Path Entropy Evaluation 25
4.2.6 Aggregation Function 26
4.3 BLOCK IDENTIFICATION 27
4.3.1 Block Marker 27
4.3.2 Block Refinement 27
4.3.3 The relationship between EEM and Block Identification 31
5. EXPERIMENT AND RESULT 33
5.1 TAG ANALYSIS 33
5.1.1 Block Tag Analysis 33
5.1.2 CSS Tag Analysis 36
5.2 SYSTEM MODULES PERFORMANCE EVALUATION 38
5.2.1 Visual Tree Evaluation 39
5.2.2 Entropy Evaluation Model Comparison 39
5.2.3 Block Non-refinement Evaluation 41
5.3 THE PERFORMANCE OF CB EXTRACTION 44
5.4 SEARCH ENGINE RESULTS EVALUATION 46
5.5 BLOCK THRESHOLDS EVALUATION 47
5.6 ITERATIVE AND NON-ITERATIVE EVALUATION 49
6. CONCLUSIONS AND FUTURE WORK 50
7. REFERENCES 51

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