Automatic Metadata Generation forWeb Pages Using a Text Mining Approach

Authors:
Hsin-Chang Yang;Chung-Hong Lee
Affiliations:
Chang Jung University Department of Information Management Tainan, Taiwan;National Kaohsiung University of Applied Sciences Department of Electrical Engineering Kaohsiung, Taiwan
Venue:
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
Year:
2005

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Abstract

The Semantic Web has emerged to replace the World Wide Web (WWW or the Web) as the unique platform for information sharing. Applications such as e-commerce will be and could be plausible only if we can annotate the Web pages with their semantics. For newly developed Semantic Web resources, such annotation can be done manually or by help of some authoring tools. However, it is not practical to semantically annotating existing Web pages due to the gigantic amount of them. To overcome this difficulty, we propose a machine learning approach to automatically generate semantic metadata for Web pages. The proposed automated process adopts the self-organizing map algorithm to cluster training Web pages and conducts a text mining process to discover some semantic descriptions about the Web pages. Preliminary experiments show that our method may generate semantically relevant metadata for the Web pages.