Package org.aksw.limes.core.evaluation.qualititativeMeasures

Class PseudoRefFMeasure

  • All Implemented Interfaces:
    IQualitativeMeasure
    public class PseudoRefFMeasure
extends PseudoFMeasure
    Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference F-Measure.
    Thereby, not relying on any gold standard. The basic idea is to measure the quality of the given Mapping by calculating how close it is to an assumed 1-to-1 Mapping between source and target.
    Since:
    1.0
    Version:
    1.0
    Author:
    Klaus Lyko (lyko@informatik.uni-leipzig.de), Axel-C. Ngonga Ngomo (ngonga@informatik.uni-leipzig.de), Mofeed Hassan (mounir@informatik.uni-leipzig.de)

    • Constructor Detail

      • PseudoRefFMeasure

        public PseudoRefFMeasure()

    • Method Detail

      • recall

        public double recall​(AMapping predictions,
                     GoldStandard goldStandard)
        The method calculates the pseudo reference recall of the machine learning predictions compared to a gold standard
        Overrides:
        recall in class PseudoFMeasure
        Parameters:
        predictions - The predictions provided by a machine learning algorithm
        goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs
        Returns:
        double - This returns the calculated reference pseudo recall

      • precision

        public double precision​(AMapping predictions,
                        GoldStandard goldStandard)
        The method calculates the pseudo reference precision of the machine learning predictions compared to a gold standard
        Overrides:
        precision in class PseudoFMeasure
        Parameters:
        predictions - The predictions provided by a machine learning algorithm
        goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs
        Returns:
        double - This returns the calculated pseudo reference precision