Explaining Clusters with Inductive Logic Programming and Linked Data

Presented at: The 12th International Semantic Web Conference (ISWC2013)

by Ilaria Tiddi, Mathieu D'Aquin, Enrico Motta

Knowledge Discovery consists in discovering hidden regulari- ties in large amount of data using data mining techniques. The obtained patterns require an interpretation that is usually achieved using some background knowledge given by experts from several domains. On the other hand, the rise of Linked Data has increased the number of con- nected cross-disciplinary knowledge, in the form of RDF datasets, classes and relationships. Here we show how Linked Data can be used in an Inductive Logic Programming process, where they provide background knowledge for finding hypotheses regarding the unrevealed connections between items of a cluster. By using an example with clusters of books, we show how different Linked Data sources can be used to automatically generate rules giving an underlying explanation to such clusters.


Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc/2013/poster-demo-proceedings/paper-49


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