Study on the Application of Topic Maps in Knowledge Mining
Date Issued
2004
Date
2004
Author(s)
Chou, Chung Chiang
DOI
zh-TW
Abstract
Internet is the biggest, unstructured database in the world today. It's easy for everyone to get information or knowledge from Internet. The Hyper-Text Markup Language (HTML) created by Tim Berners-Lee makes this situation come true. Everyone can publish their web pages on Internet, but no well-structured content description language can be used. That makes it harder and harder to get useful information or knowledge from Internet. The obvious example is HTML. Tags have less content description mechanism. That's why modern search engine such as Google or Yahoo! can only use keyword matching to find out lots of web pages, but useful web pages is very few.
The Semantic Web is next generation technology to solve this problem. It's focus on content description. Every shared resource should be given semantic description such that search engine or user agent can "understand" what the resource is and improve the precision of search results, but it's not an easy job. For getting more useful information or knowledge, it’s a possible way to combine recommended content description language we have now, data-mining technologies to find information or knowledge, and structure a more semantic result for user.
In this paper, XTM (XML Topic Maps) is a content description language to describe found data or information. Some specifications, mining technologies and software are learned also. We combine these to create a knowledge mining template to mine the useful, well-organized information or knowledge from Internet.
The Semantic Web is next generation technology to solve this problem. It's focus on content description. Every shared resource should be given semantic description such that search engine or user agent can "understand" what the resource is and improve the precision of search results, but it's not an easy job. For getting more useful information or knowledge, it’s a possible way to combine recommended content description language we have now, data-mining technologies to find information or knowledge, and structure a more semantic result for user.
In this paper, XTM (XML Topic Maps) is a content description language to describe found data or information. Some specifications, mining technologies and software are learned also. We combine these to create a knowledge mining template to mine the useful, well-organized information or knowledge from Internet.
Subjects
知識礦掘
延伸式標籤語言
主題地圖
Knowledge Mining
Topic Maps
Extensible Markup Language
Type
thesis