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dc.contributor.authorDong, Hai
dc.contributor.authorHussain, F.
dc.identifier.citationDong, H. and Hussain, F. 2014. Self-adaptive semantic focused crawler for mining services information discovery. IEEE Transactions on Industrial Informatics. 10 (2): pp. 1616-1626.

It is well recognized that the Internet has become the largest marketplace in the world, and online advertising is very popular with numerous industries, including the traditional mining service industry where mining service advertisements are effective carriers of mining service information. However, service users may encounter three major issues - heterogeneity, ubiquity, and ambiguity, when searching for mining service information over the Internet. In this paper, we present the framework of a novel self-adaptive semantic focused crawler - SASF crawler, with the purpose of precisely and efficiently discovering, formatting, and indexing mining service information over the Internet, by taking into account the three major issues. This framework incorporates the technologies of semantic focused crawling and ontology learning, in order to maintain the performance of this crawler, regardless of the variety in the Web environment. The innovations of this research lie in the design of an unsupervised framework for vocabulary-based ontology learning, and a hybrid algorithm for matching semantically relevant concepts and metadata. A series of experiments are conducted in order to evaluate the performance of this crawler. The conclusion and the direction of future work are given in the final section. © 2012 IEEE.

dc.publisherIEEE Computer Society
dc.titleSelf-adaptive semantic focused crawler for mining services information discovery
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Industrial Informatics
curtin.departmentSchool of Accounting
curtin.accessStatusFulltext not available

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