An Automatic Semantic-Segment Detection Method in the HTML Language

• Main Authors: Tzu-chen Tsai, 蔡子宸
• Other Authors: 楊鎮華
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/46503119169264712345

碩士 === 國立中央大學 === 資訊工程研究所 === 95 === 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.