Semantic clustering (with k-medoids) of SPARQL query results over RDF version of videolectures.net dataset

The workflow uses RapidMiner extension named RMonto (http://semantic.cs.put.poznan.pl/RMonto/) to perform clustering of SPARQL query results based on chosen semantic similarity measure. Since the semantics of the backgound ontology is used in this way, we use the name "semantic clustering". The SPARQL query is entered in a parameter of "SPARQL selector" operator. The clustering operator (k-medoids) allows to specify which of the query variables are to be used as clustering criteria. If more than one variable is used than the results are clustered such that kind of multifaceted hierarchy is computed over them, which is dynamically determined. More on such functionality may be found in [1]. The parameters of the operator allow to choose URI attributes (query variables that are bound to OWL classes), and "normal" attributes that is variables that are bound to literal values (numerical, strings, etc.). Operator "Build knowledge base" is responsible for collecting data either from OWL files or SPARQL endpoints or RDF repositories and provide it to the subsequent operators in a workflow. In this workflow, this operator retrieves the data from a Sesame/OWLIM repository whose address is given as the operator's parameter. The repository stores the Digital Multimedia Repositories Ontology (DMRO), and DMRO-based RDF version of videolectures.net dataset (see: http://www.e-lico.eu/?q=node/288, http://www.ecmlpkdd2011.org/challenge.php).

[1] Lawrynowicz A., Potoniec J., Konieczny L., Madziar M., Nowak A. and K. T. Pawlak: ASPARAGUS - A System for Automatic SPARQL Query Results Aggregation Using Semantics. In ICCCI (1). Vol. 6922 of LNCS, Springer, (2011): 304-313