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dc.contributor.authorDong, Hai
dc.contributor.authorHussain, Farookh
dc.identifier.citationDong, Hai and Hussain, Farookh Khadeer. 2013. SOF: a semi-supervised ontology - learning - based focused crawler. Concurrency and Computation: Practice and Experience. 25 (12): pp. 1755-1770.

The rapid increase in the volume of data available on the Internet makes it increasingly impractical for a crawler to index the whole Web. Instead, many intelligent crawlers, known as ontology-based semantic focused crawlers, have been designed by making use of Semantic Web technologies for topic-centered Web information crawling. Ontologies, however, have constraints of validity and time, which may influence the performance of the crawlers. Ontology-learning-based focused crawlers are therefore designed to automatically evolve ontologies by integrating ontology learning technologies. Nevertheless, surveys indicate that the existing ontology-learning-based focused crawlers do not have the capability to automatically enrich the content of ontologies, which makes these crawlers unreliable in the open and heterogeneous Web environment. Hence, in this paper, we propose a framework for a novel semi-supervised ontology-learning-based focused (SOF) crawler, the SOF crawler, which embodies a series of schemas for ontology generation and Web information formatting, a semi-supervised ontology learning framework, and a hybrid Web page classification approach aggregated by a group of support vector machine models. A series of tests are implemented to evaluate the technical feasibility of this proposed framework. The conclusion and the future work are summarized in the final section.

dc.publisherJohn Wiley & Sons Ltd
dc.subjectsupport vector machine
dc.subjectprobabilistic model
dc.subjectontology-learning-based focused crawler
dc.subjectsemantic focused crawler
dc.subjectontological term learning
dc.subjectsemi-supervised ontology learning
dc.subjectsemantic similarity model
dc.titleSOF: a semi-supervised ontology - learning - based focused crawler
dc.typeJournal Article
dcterms.source.titleConcurrency and Computation: Practice and Experience
curtin.accessStatusFulltext not available

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