Ontology-Learning-Based focused crawling for online service advertising information discovery and classification

Authors:
Hai Dong;Farookh Khadeer Hussain;Elizabeth Chang
Affiliations:
School of Information Systems, Curtin University of Technology, Australia;School of Software, University of Technology, Sydney, Australia;School of Information Systems, Curtin University of Technology, Australia
Venue:
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Year:
2012

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Abstract

Online advertising has become increasingly popular among SMEs in service industries, and thousands of service advertisements are published on the Internet every day. However, there is a huge barrier between service-provider-oriented service information publishing and service-customer-oriented service information discovery, which causes that service consumers hardly retrieve the published service advertising information from the Internet. This issue is partly resulted from the ubiquitous, heterogeneous, and ambiguous service advertising information and the open and shoreless Web environment. The existing research, nevertheless, rarely focuses on this research problem. In this paper, we propose an ontology-learning-based focused crawling approach, enabling Web-crawler-based online service advertising information discovery and classification in the Web environment, by taking into account the characteristics of service advertising information. This approach integrates an ontology-based focused crawling framework, a vocabulary-based ontology learning framework, and a hybrid mathematical model for service advertising information similarity computation.