Package de.uni_mannheim.informatik.dws.melt.matching_jena_matchers.metalevel

Class MachineLearningWEKAFilter

java.lang.Object
eu.sealsproject.platform.res.tool.impl.AbstractPlugin
de.uni_mannheim.informatik.dws.melt.matching_base.MatcherURL
de.uni_mannheim.informatik.dws.melt.matching_base.MatcherFile
de.uni_mannheim.informatik.dws.melt.matching_jena.MatcherYAAA
de.uni_mannheim.informatik.dws.melt.matching_jena.MatcherYAAAJena
de.uni_mannheim.informatik.dws.melt.matching_jena_matchers.metalevel.MachineLearningWEKAFilter
All Implemented Interfaces:
IMatcher<org.apache.jena.ontology.OntModel,Alignment,Properties>, eu.sealsproject.platform.res.domain.omt.IOntologyMatchingToolBridge, eu.sealsproject.platform.res.tool.api.IPlugin, eu.sealsproject.platform.res.tool.api.IToolBridge
public class MachineLearningWEKAFilter extends MatcherYAAAJena
Non functional code.

  • Field Details

    • LOGGER

      private static final org.slf4j.Logger LOGGER
      Default logger.

    • trainingGenerator

      private MatcherYAAAJena trainingGenerator
      Generator for training data. If relation is equivalence, then this is the positive class. All other relations are the negative class.

    • confidenceNames

      private List<String> confidenceNames
      Which additional confidences should be used to train the classifier.

    • crossValidationNumber

      private int crossValidationNumber
      Number of cross validation to execute.

    • numberOfParallelJobs

      private int numberOfParallelJobs
      Number of jobs to execute in parallel.

  • Constructor Details

    • MachineLearningWEKAFilter

      public MachineLearningWEKAFilter()

    • MachineLearningWEKAFilter

      public MachineLearningWEKAFilter(Alignment trainingAlignment)

    • MachineLearningWEKAFilter

      public MachineLearningWEKAFilter(Alignment trainingAlignment, int crossValidationNumber, int numberOfParallelJobs)

    • MachineLearningWEKAFilter

      public MachineLearningWEKAFilter(MatcherYAAAJena trainingGenerator)

    • MachineLearningWEKAFilter

      public MachineLearningWEKAFilter(MatcherYAAAJena trainingGenerator, List<String> confidenceNames)

    • MachineLearningWEKAFilter

      public MachineLearningWEKAFilter(MatcherYAAAJena trainingGenerator, int crossValidationNumber, int numberOfParallelJobs)

    • MachineLearningWEKAFilter

      public MachineLearningWEKAFilter(MatcherYAAAJena trainingGenerator, List<String> confidenceNames, int crossValidationNumber, int numberOfParallelJobs)
      Constructor
      Parameters:
      trainingGenerator - generator for training data.
      confidenceNames - confidence names to use.
      crossValidationNumber - Number of cross validation to execute.
      numberOfParallelJobs - Number of jobs to execute in parallel.

  • Method Details

    • match

      public Alignment match(org.apache.jena.ontology.OntModel source, org.apache.jena.ontology.OntModel target, Alignment inputAlignment, Properties properties) throws Exception
      Description copied from class: MatcherYAAAJena
      Aligns two ontologies specified via a Jena OntModel, with an input alignment as Alignment object, and returns the mapping of the resulting alignment. Note: This method might be called multiple times in a row when using the evaluation framework. Make sure to return a mapping which is specific to the given inputs.
      Specified by:
      match in interface IMatcher<org.apache.jena.ontology.OntModel,Alignment,Properties>
      Specified by:
      match in class MatcherYAAAJena
      Parameters:
      source - This OntModel represents the source ontology.
      target - This OntModel represents the target ontology.
      inputAlignment - This mapping represents the input alignment.
      properties - Additional properties.
      Returns:
      The resulting alignment of the matching process.
      Throws:
      Exception - Any exception which occurs during matching.

    • trainModel

      public File trainModel(Alignment trainingAlignment) throws Exception
      Throws:
      Exception

    • applyModel

      public Alignment applyModel(File model, Alignment alignment)

    • getTrainingInstances

      public weka.core.Instances getTrainingInstances(Alignment alignment)
      Generates the weka instances which can be used for training a model .
      Parameters:
      alignment - Dataset to write. Correspondences with an EQUIVALENCE relation are treated as positives. All other relations are treated as negatives.
      Returns:
      the weka instances object

    • getTestInstances

      public weka.core.Instances getTestInstances(Collection<Correspondence> alignment)
      Generates the weka instances which can be used for predicting unseen examples.
      Parameters:
      alignment - the correspondences which should be predicted.
      Returns:
      the weka instances object

    • writeArffFile

      public void writeArffFile(weka.core.Instances data, File file)
      Helper method to write an arff formatted file.
      Parameters:
      data - the instances to write to file
      file - the file object

    • getConfidenceNames

      public List<String> getConfidenceNames()