Pack: RMonto pack

Description:

- Repositories of RapidMiner baseline workflows, and used datasets

- DMOP ontology files from ver5.2

-input files to meta-mining workflows 

Description:

For the information on the ontology see: http://www.e-lico.eu/?q=node/288

For the information on the original dataset see: 

http://www.ecmlpkdd2011.org/challenge.php  

The ontology and KB files are zipped into one file. 

Description:

No description available

Description:

The workflow uses RapidMiner extension named RMonto (http://semantic.cs.put.poznan.pl/RMonto/). 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 it is parametrized in this way, that is builds a Sesame/OWLIM repository from the files specified in "Load file" operators. Paths to OWL files are specified as parameter values in "Load file" operators, and path to the Sesame/OWLIM repository in "Build knowledge base" operator.

After execution of the workflow the repository will store 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) as a Sesame/OWLIM repository.

The files referred to in "Load file" operators are included in myExperiment's RMonto pack as a zipped file.

A repository to be created (referred to in "Build knowledge base" operator) is referenced in parameters of some other workflows in the RMonto pack.

Description:

Performs a crossvalidation on a data set composed of baseline RapidMiner workflows described with dataset characteristics used by the given workflow and the learning algorithm used in the given workflow.

Description:

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

Description:

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 ("Epistemic" kernel from [1]). Since the semantics of the backgound ontology is used in this way, we use the name "semantic clustering". This particular kernel is based on the commitee of features (ontology classes) that are specified in the paremeter of "Create TBox features" operator. The SPARQL query is entered in a parameter of "SPARQL selector" operator. The clustering operator (alpha-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 [3]. 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] C. d'Amato, N. Fanizzi, F. Esposito.  Non-parametric Statistical Learning Methods for Inductive Classifiers in Semantic Knowledge Bases.   ICSC 2008, IEEE International Conference on Semantic Computing, August 4-7, 2008, Santa Clara, CA, USA.

[2] G. Antzoulatos, M. N. Vrahatis: Alpha-Clusterable Sets. ECML/PKDD (1) 2011: 108-123

[3] 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

Description:

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

Description:

Performs a crossvalidation on a data set composed of meta-data of baseline RapidMiner workflows expressed in RDF with the DMOP's ontology terminology for representing processes. Includes discovery of a set of semantic features (patterns) by the Fr-ONT-Qu algorithm (‘workflow patterns’). Through propositionalisation approach those features may be used in an arbitrary (propositional) RapidMiner classification operator.