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

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  • Francesca A. Lisi

    (Università degli Studi di Bari “Aldo Moro”, Italy)

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.

Suggested Citation

  • Francesca A. Lisi, 2011. "AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 7(3), pages 1-22, July.
    • Handle: RePEc:igg:jswis0:v:7:y:2011:i:3:p:1-22
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