自動偵測HTML語言的語意區塊

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姓名

蔡子宸(Tzu-chen Tsai) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

自動偵測HTML語言的語意區塊
(An Automatic Semantic-Segment Detection Method in the HTML Language)

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摘要(中)

21世紀網際網路的使用率已迅速普及化，以及各大場商推出多樣化的可攜式上網設備，讓使用者除了傳統使用個人電腦的設備外，增加了更多的選擇及機會，達到可以隨時隨地使用各種上網設備，在網路上獲得網頁資訊及學習。雖然這些可攜式上網設備的優勢就是輕便、小巧、可移動性強，及功能多樣化，深受大眾喜愛。但是也存在讓人困擾的缺點，如受到螢幕畫面太小會導致呈現網頁內容時，常常出現排版失當及不易閱讀的情況，以及網路頻寬和計算能力較個人電腦差，產生使用者需要花較多的等待時間...等問題。然而內容調適主要技術是分析、拆解原始網頁並且依照使用者的情境狀態、身體狀況及設備等條件，重新為使用者轉換內容，產生量身訂做的網頁，使得調適後的網頁是以更佳的呈現方式，表達作者要傳遞給使用者的資訊。總而言之，內容調適(Content Adaptation)機制是為了彌補各大網站未提供使用者適宜網頁的缺失，進行自動化產生調適網頁的系統。但是系統依照使用者條件調適網頁前，必需先使用正確的編碼拆解網頁。因此，我在論文中提出一套有效率模組化拆解且自動偵測語意區塊的方法，進行識別最小不可再被細分的成員，依照語意關聯性和結構性，自動偵測出語意區塊，作為調適的單位，而單位內的物件要保留語意的同質關聯性、完整的功能性、可讀性，以及呈現位置結構的階層性。然而再以語意區塊為單位進行物件轉換等調適策略，產生適合使用者的網頁，解決等使用者等待下載時間過長及觀看網頁資訊過多導致需要不斷移動畫面所產生的不易閱讀等等的問題。

摘要(英)

The amount of information on World Wide Web continues to grow at an astonishing speed increases astonishingly, and then many contents of the web pages are designed for large-sized screen and powerful computation device such as PC and NB so these contents can not fit into the small device, such as personal digital assistants. Additionally, these factors, users’ personal condition and capability of device, can influence the users to successfully understand content of the webpage. In this paper, we propose a mediator system to facilitate the surfing in WWW for users. The main purpose of this system adapts the original content to suitable content for users via Context Aware. We named this system Content Adaptation (CA). In other words, CA system produces the suitable webpage for the user.
CA can be separated into two steps, content decomposing and content re-composing. Because of the content decomposer needs to analyze semantics of HTML language before adapting content for the users’ condition, I focused on the automatic content decomposition in my research. In the decomposition process, I need to use a correct Code-Page to parse the HTML file and structurally consider whole tags and information of HTML, furthermore I developed to analyze the semantic context, architecture, arrangement, structure, and visual effect and split it into a small Semantic Segment (S.S.) that is not being subdivided. S.S. has some important properties, keeping complete function (functionality related), readable typesetting (readability related), relationship of presenting (space and time related), and literary context (semantics related). My experimental results show that I proposed convention of detection semantic segments and developed a page splitting scheme to partition the web page into many smaller semantic segments greatly improve the users’ browsing experiences on a small screen of hand-held devices.

關鍵字(中)

★ 內容調適
★ 語意區塊
★ 調適策略
★ 自動偵測

關鍵字(英)

★ Semantic Segment
★ Content Adaptation
★ Structure Fragment
★ Context Aware

論文目次

摘要 I
ABSTRACT II
ACKNOWLEDGEMENTS IV
CONTENTS V
LIST OF FIGURES VIII
LIST OF TABLES XI
CHAPTER 1 INTRODUCTION 1
1.1 WHAT IS THE MOTIVATION OF THIS RESEARCH? 1
1.2 WHAT KINDS OF PROBLEMS TO BE SOLVED? 4
1.3 CHARACTERISTICS AND CHALLENGES 6
1.4 HOW TO SOLVE THE PROBLEMS? 7
1.5 CONTRIBUTION OF OUR SOLUTIONS 8
CHAPTER 2 RELATED WORK 9
CHAPTER 3 METHOD AND SOLUTION 17
3.1 ENCODING AND CONVERSION 19
3.1.1 Encoding and conversion methodology 19
3.1.2 Encoding and conversion algorithm 19
3.2 CSS LOCALIZING AND PARSING HTML 20
3.2.1 CSS localizing and parsing HTML methodology 20
3.2.2 CSS localizing algorithm 20
3.3 STRUCTURE-FRAGMENT IDENTIFICATION 21
3.3.1 Structure-fragment identification methodology 21
3.3.1.1 Definition 22
3.4 SEMANTIC-SEGMENT DETECTION 42
3.4.1 Semantic-segment detection methodology 42
3.4.1.1 Definition 42
CHAPTER 4 SYSTEM IMPLEMENTATION 43
4.1 DEVELOPMENT PHILOSOPHY 43
4.2 SYSTEM ARCHITECTURE 44
4.2.1 High-level system design and analysis 49
4.2.2 Low-level system design and Analysis 51
4.3 SYSTEM DEMO 56
4.3.1 Text Level demo 56
4.3.2 Tag Level demo 58
4.3.3 Structure Level demo 58
4.3.4 Semantic Level demo 59
4.4 EXPERIENCE LEARNED FORM THE IMPLEMENTATION 60
CHAPTER 5 EXPERIMENT AND DISCUSSION 63
5.1 BACKGROUND 63
5.2 QUANTITATIVE EVALUATION 64
5.2.1 Performance evaluation 64
5.2.1.1 System Execution time 64
5.2.1.2 Precision 68
5.2.2 Results and lesson learned 70
5.3 QUALITATIVE EVALUATION 78
5.3.1 Questionnaire design and survey 78
5.3.2 Results and lesson learned 79
5.3.2.1 Performance comparison with and without my approach 79
5.3.2.2 Performance comparison with other related approaches 81
CHAPTER 6 CONCLUSION AND FUTURE WORK 83
6.1 CONCLUSIONS 83
6.2 FUTURE WORK 84
REFERENCE 86

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指導教授

楊鎮華(Stephen J.H. Yang)

審核日期

2007-7-5

推文