Abstract
The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach.
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Nebot, V., Berlanga, R. (2010). Mining Association Rules from Semantic Web Data. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_52
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DOI: https://doi.org/10.1007/978-3-642-13025-0_52
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