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URN etd-0804108-101831 Statistics This thesis had been viewed 3623 times. Download 1691 times. Author Yi-De Jin Author's Email Address firstname.lastname@example.org Department Computer Science and Enginerring Year 2007 Semester 2 Degree Master Type of Document Master's Thesis Language zh-TW.Big5 Chinese Page Count 45 Title Machine Learning Approach to Information Extraction For The Creation of Metadata in Semantic Web Keyword Semantic Web Information Extraction Machine Learning Machine Learning Information Extraction Semantic Web Abstract With the problem of information explosion on the web, people need an efficient way to extract the information they really need. Semantic web is an emerging technology working by building a metadata layer upon the current web and using the metadata description language to describe the resources on the WWW. It is an extension of current Web where information is given well-defined meaning, better, enabling computers and people to process in cooperation.
In this thesis, we design and implement a system that is able to extract the chinese documents and to provide the semantic service. The architecture consists of two parts: Chinese Extraction Components and Service Front End. The Back End consists of several components used to extract the Chinese documents and use Machine Learning to build Chinese grammar structural. The Service Front End provides several semantic services. After building the whole system, we make the evaluation for our system by extract some specific domain events from the relevant documents and figure out which reasons can influence the result of extraction.
Advisor Committee Ching-Long Yeh - advisor
Chen-Chiung Hsieh - co-chair
none - co-chair
Files Date of Defense 2008-07-30 Date of Submission 2008-08-05