org.aksw.limes.core.evaluation.qualititativeMeasures

Class PseudoRefFMeasure

java.lang.Object

org.aksw.limes.core.evaluation.qualititativeMeasures.APRF

org.aksw.limes.core.evaluation.qualititativeMeasures.APseudoPRF

org.aksw.limes.core.evaluation.qualititativeMeasures.PseudoFMeasure

org.aksw.limes.core.evaluation.qualititativeMeasures.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)

Field Summary

Fields inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.APseudoPRF

symmetricPrecision

Constructor Summary

Constructors

Constructor and Description
PseudoRefFMeasure()

Method Summary

Modifier and Type	Method and Description
double	calculate(AMapping predictions, GoldStandard goldStandard, double beta) The method calculates the pseudo reference F-Measure of the machine learning predictions compared to a gold standard for beta = 1 .
double	precision(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo reference precision of the machine learning predictions compared to a gold standard
double	recall(AMapping predictions, GoldStandard goldStandard) The method calculates the pseudo reference recall of the machine learning predictions compared to a gold standard

Methods inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.PseudoFMeasure

calculate, getPseudoFMeasure

Methods inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.APseudoPRF

getUse1To1Mapping, isSymmetricPrecision, isUse1To1Mapping, setSymmetricPrecision, setUse1To1Mapping

Methods inherited from class org.aksw.limes.core.evaluation.qualititativeMeasures.APRF

falseNegative, trueFalsePositive, trueNegative

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PseudoRefFMeasure

public PseudoRefFMeasure()

Method Detail

calculate

public double calculate(AMapping predictions,
                        GoldStandard goldStandard,
                        double beta)

The method calculates the pseudo reference F-Measure of the machine learning predictions compared to a gold standard for beta = 1 .

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.

beta - this values specifies how F-Measure is biased between precision and recall

Returns:

double - This returns the calculated pseudo reference F-Measure.

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