HW-STALKER: A Machine Learning-Based Approach to Transform Hidden Web Data to XML
Abstract
In this paper, we propose an algorithm called HW-Transform for transforming hidden web data to XML format using machine learning by extending STALKER to handle hyperlinked hidden web pages. One of the key features of our approach is that we identify and transform key attributes of query results into XML attributes. These key attributes facilitate applications such as change detection and data integration, by efficiently identifying related or identical results. based on the proposed algorithm, we have implemented a prototype system called HW-STALKER using Java. Our experiments demonstrate that HW-Transform shows acceptable performance for transforming query results to XML. © Springer-Verlag Berlin Heidelberg 2004.
Recommended Citation
V. Kovalev et al., "HW-STALKER: A Machine Learning-Based Approach to Transform Hidden Web Data to XML," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3180, pp. 936 - 946, Springer, Jan 2004.
The definitive version is available at https://doi.org/10.1007/978-3-540-30075-5_90
Department(s)
Computer Science
International Standard Book Number (ISBN)
978-354022936-0
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2004
