Automatic Hidden Web Database Classification

Authors:
Zhiguo Gong;Jingbai Zhang;Qian Liu
Affiliations:
Faculty of Science and Technology, University of Macau, Macao, PRC;Faculty of Science and Technology, University of Macau, Macao, PRC;Faculty of Science and Technology, University of Macau, Macao, PRC
Venue:
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2007

Citing 8
Cited 0

An example-based mapping method for text categorization and retrieval

ACM Transactions on Information Systems (TOIS)
The nature of statistical learning theory

The nature of statistical learning theory
Probe, count, and classify: categorizing hidden web databases

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
QProber: A system for automatic classification of hidden-Web databases

ACM Transactions on Information Systems (TOIS)
Crawling the Hidden Web

Proceedings of the 27th International Conference on Very Large Data Bases
Automatic Information Discovery from the "Invisible Web"

ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
Crawling for Domain-Speci.c Hidden Web Resources

WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
Learning trees and rules with set-valued features

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a method for automatic classification of Hidden-Web databases is addressed. In our approach, the classification tree for Hidden Web databases is constructed by tailoring the well accepted classification tree of DMOZ Directory. Then the feature for each class is extracted from randomly selected Web documents in the corresponding category. For each Web database, query terms are selected from the class features based on their weights. A hidden-web database is then probed by analyzing the results of the class-specific query. To raise the performance further, we also use Web pages which have links pointing to the hidden-web database (HW-DB) as another important source to represent the database. We combine link-based evaluation and query-based probing as our final classification solution. The experiment shows that the combined method can produce much better performance for classification of hidden Web Databases.