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AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining

AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining

Francesca A. Lisi
Copyright: © 2011 |Volume: 7 |Issue: 3 |Pages: 22
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781613509852|DOI: 10.4018/jswis.2011070101
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MLA

Lisi, Francesca A. "AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining." IJSWIS vol.7, no.3 2011: pp.1-22. http://doi.org/10.4018/jswis.2011070101

APA

Lisi, F. A. (2011). AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining. International Journal on Semantic Web and Information Systems (IJSWIS), 7(3), 1-22. http://doi.org/10.4018/jswis.2011070101

Chicago

Lisi, Francesca A. "AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining," International Journal on Semantic Web and Information Systems (IJSWIS) 7, no.3: 1-22. http://doi.org/10.4018/jswis.2011070101

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Abstract

Onto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system -QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book.

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