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A comparative study of web resource mining algorithms for one‐stop learning

A comparative study of web resource mining algorithms for one‐stop learning Web resource mining for one‐stop learning is an effort to turn the Web into a convenient and valuable resource for education for the self‐motivated, knowledge seeking student. It is aimed at providing an efficient and effective algorithm to generate an extremely small set of self‐contained Web pages which are adequate for the student to study well about the technical subject of her choice on her own pace without requiring clicking through numerous linked resources. In this paper, we present three different scoring measures which can be plugged into such an algorithm designed for the objective stated above. We also demonstrate the effectiveness of the algorithms proposed in this paper which are equipped with a choice of the three scoring measures by showing their promising experimental results. Our algorithms achieved up to 87% of precision in average in automatically finding relatively suitable Web resources for one‐stop learning as opposed to 9% of precision offered by general purpose search engines. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

A comparative study of web resource mining algorithms for one‐stop learning

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Publisher
Emerald Publishing
Copyright
Copyright © 2006 Emerald Group Publishing Limited. All rights reserved.
ISSN
1744-0084
DOI
10.1108/17440080680000103
Publisher site
See Article on Publisher Site

Abstract

Web resource mining for one‐stop learning is an effort to turn the Web into a convenient and valuable resource for education for the self‐motivated, knowledge seeking student. It is aimed at providing an efficient and effective algorithm to generate an extremely small set of self‐contained Web pages which are adequate for the student to study well about the technical subject of her choice on her own pace without requiring clicking through numerous linked resources. In this paper, we present three different scoring measures which can be plugged into such an algorithm designed for the objective stated above. We also demonstrate the effectiveness of the algorithms proposed in this paper which are equipped with a choice of the three scoring measures by showing their promising experimental results. Our algorithms achieved up to 87% of precision in average in automatically finding relatively suitable Web resources for one‐stop learning as opposed to 9% of precision offered by general purpose search engines.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: May 1, 2006

Keywords: Web content mining; Web filtering; Information retrieval; One‐stop learning; Web‐based learning

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