myExperiment - Content

Taverna 2

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James Eales

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This workflow will remove any XML-invalid and non-ASCII characters (e.g. for sending to the ASCII-only Termine service) from any text supplied to the input port. This is a workflow component, designed to be used as a nested workflow inside a larger text mining or text processing workflow.

Created: 2010-02-18 | Last updated: 2011-12-13

Credits: James Eales

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Matko BoÅ¡njak

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e-LICO recommender workflows

Created: 2011-03-15 15:33:48 | Last updated: 2012-01-28 19:39:06

This pack contains recommender system workflows created for the purpose of e-LICO project.

6 items in this pack

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Taverna 2

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James Eales

Clean plain text (1)

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This workflow will remove any XML-invalid characters (these characters often appear in the output of PDF to text software) from any text supplied to the input port. This is a workflow component, designed to be used as a nested workflow inside a larger text mining or text processing workflow.

Created: 2010-02-18 | Last updated: 2011-12-13

Credits: James Eales

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Ninoaf

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Recommender systems workflow templates 2012

Created: 2012-01-08 12:27:43 | Last updated: 2012-06-03 19:48:22

The Recommender Extension can be downloaded from the Rapid-I Marketplace from: http://rapidupdate.de:8180/UpdateServer/faces/product_details.xhtml?productId=rmx_irbrecommender . More details can be found: http://elico.rapid-i.com/recommender-extension.html

12 items in this pack

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Taverna 2

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Simon Jupp

miRNA GFF to entrez gene (1)

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This workflow reads a GFF file of miRNA cooridinates and uses BioMart to search human ensemble genes for the gene that codes for the miRNA. The workflow returns a list of miRNAid, chromosome, start, stop, strand, entrez gene id, gene name, gene strand. Example input file here: ftp://mirbase.org/pub/mirbase/CURRENT/genomes/hsa.gff

Created: 2011-01-26 | Last updated: 2012-01-11

RapidMiner

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Sebastian land

Using Remember / Recall for "tunneling" re... (1)

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This process shows how Remeber and Recall operators can be used for passing results from one position to another position in the process, when it's impossible to make a direct connection. This process introduces another advanced RapidMiner technique: The macro handling. We have used the predefined macro a, accessed by %{a}, that gives the apply count of the operator. So we are remembering each application of the models that are generated in the learning subprocess of the Split validation. Af...

Created: 2010-04-29 | Last updated: 2012-01-16

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Lawrynka

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Data supplementary to meta-mining workflows

Created: 2012-03-05 22:22:33 | Last updated: 2012-03-05 22:23:51

Credits: Lawrynka

License: Creative Commons Attribution-Share Alike 3.0 Unported License

- Repositories of RapidMiner baseline workflows, and used datasets - DMOP ontology files from ver5.2 -input files to meta-mining workflows

File type: ZIP archive

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Lawrynka

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Digital Multimedia Repositories Ontology (DMRO) and ...

Created: 2012-01-29 16:35:26 | Last updated: 2012-01-29 16:38:25

Credits: Lawrynka

License: Creative Commons Attribution-Share Alike 3.0 Unported License

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.

File type: ZIP archive

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RapidMiner

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Lawrynka

Loading OWL files (RDF version of videolec... (1)

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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 va...

Created: 2012-01-29 | Last updated: 2012-01-29

RapidMiner

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Lawrynka

Semantic clustering (with alpha-clustering... (1)

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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 ...

Created: 2012-01-29 | Last updated: 2012-01-30

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