Workflow Entry: Semantic clustering (with AHC) 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. The measure used in this particualr workflow is a kernel that exploits membership of clustered individuals to OWL classes from a background ontology ("Common classes" kernel from [1]). 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 (agglomerative hierarchical clustering) 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 in the way that kind of multifaceted hierarchy is computed over them, which is dynamically determined based on the membership of individuals to OWL classes. More on such functionality may be found in [2]. 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] Bloehdorn S, Sure Y. Kernel methods for mining instance data in ontologies. In Proceedings of the 6th International Semantic Web Conference and the 2nd Asian Semantic Web Conference (ISWC 2007 + ASWC 2007), vol. 4825 of LNCS, pages 58–71. Springer, Berlin–Heidelberg, Germany, 2007.

[2] 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