Workflows

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Showing 7 results. Use the filters on the left and the search box below to refine the results.
Type: RapidMiner Tag: semantic web
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Workflow 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

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Workflow 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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Workflow Semantic clustering (with AHC) of SPARQL q... (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 ("Common classes" kernel from [1]). Since the semantics of the backgound ontology is used in this way, we use the name "semantic clustering". ...

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

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Workflow Semantic clustering (with k-medoids) of SP... (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. 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 ...

Created: 2012-01-29

Workflow Mining Semantic Web data using Corresponde... (1)

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This workflow will explain that how an example set can be extracted from an RDF resource using the provided SPARQL query. This example set is then divided into training and test parts. These sub-example sets are used by the Correspondencce Analysis operators (encapsulate the Correspondencce Analysis data transformation technique) which processes each feature at a time and transform the data into a different space. This transformed data is more meaningful and helps the learner to improve clas...

Created: 2011-06-25 | Last updated: 2011-06-25

Workflow Mining Semantic Web data using Corresponde... (1)

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This workflow describes how to learn from the Semantic Web's data using a data transformation algorithm 'Correspondence Analysis'. The input to the workflow is a feature vector developed from a RDF resource. The loaded example set is divided into training and test parts. These sub-example sets are used by the Correspondence Analysis operators (encapsulate the Correspondence Analysis data transformation technique) which processes each feature at a time and transform the data into a different...

Created: 2011-06-25 | Last updated: 2011-06-25

Workflow Mining Semantic Web data using FastMap - E... (1)

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This workflow describes how to learn from the Semantic Web's data. The input to the workflow is a feature vector developed from a RDF resource. The loaded example set is then divided into training and test parts. These sub-example sets are used by the FastMap operators (encapsulate the FastMap data transformation technique), which processes each feature at a time and transform the data into a different space. This transformed data is more meaningful and helps the learner to improve classfica...

Created: 2011-06-25 | Last updated: 2011-06-25

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