SOF: a semi-supervised ontology - learning - based focused crawler
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-ce...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
John Wiley & Sons Ltd
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/18523 |
| _version_ | 1848749768317075456 |
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| author | Dong, Hai Hussain, Farookh |
| author_facet | Dong, Hai Hussain, Farookh |
| author_sort | Dong, Hai |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:26:11Z |
| format | Journal Article |
| id | curtin-20.500.11937-18523 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:26:11Z |
| publishDate | 2013 |
| publisher | John Wiley & Sons Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-185232017-09-13T16:04:39Z SOF: a semi-supervised ontology - learning - based focused crawler Dong, Hai Hussain, Farookh support vector machine probabilistic model ontology-learning-based focused crawler semantic focused crawler ontological term learning semi-supervised ontology learning semantic similarity model 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. 2013 Journal Article http://hdl.handle.net/20.500.11937/18523 10.1002/cpe.2980 John Wiley & Sons Ltd restricted |
| spellingShingle | support vector machine probabilistic model ontology-learning-based focused crawler semantic focused crawler ontological term learning semi-supervised ontology learning semantic similarity model Dong, Hai Hussain, Farookh SOF: a semi-supervised ontology - learning - based focused crawler |
| title | SOF: a semi-supervised ontology - learning - based focused crawler |
| title_full | SOF: a semi-supervised ontology - learning - based focused crawler |
| title_fullStr | SOF: a semi-supervised ontology - learning - based focused crawler |
| title_full_unstemmed | SOF: a semi-supervised ontology - learning - based focused crawler |
| title_short | SOF: a semi-supervised ontology - learning - based focused crawler |
| title_sort | sof: a semi-supervised ontology - learning - based focused crawler |
| topic | support vector machine probabilistic model ontology-learning-based focused crawler semantic focused crawler ontological term learning semi-supervised ontology learning semantic similarity model |
| url | http://hdl.handle.net/20.500.11937/18523 |