org.dllearner.learningproblems
Class ClassLearningProblem

java.lang.Object
  org.dllearner.core.AbstractComponent
      org.dllearner.core.AbstractLearningProblem
          org.dllearner.learningproblems.ClassLearningProblem

All Implemented Interfaces:

Component, LearningProblem

public class ClassLearningProblem
extends AbstractLearningProblem

The problem of learning the description of an existing class in an OWL ontology.

Author:

Jens Lehmann

Constructor Summary

ClassLearningProblem(AbstractReasonerComponent reasoner)

Method Summary

ClassScore	computeScore(Description description) Computes the Score of a given class description with respect to this learning problem.
static Collection<ConfigOption<?>>	createConfigOptions()
EvaluatedDescriptionClass	evaluate(Description description) Evaluates the description by computing the score and returning an evaluated description of the correct type (ClassLearningProblem returns EvaluatedDescriptionClass instead of generic EvaluatedDescription).
boolean	followsFromKB(Description description)
double	getAccuracy(Description description) This method returns a value, which indicates how accurate a class description solves a learning problem.
double	getAccuracyOrTooWeak(Description description, double noise) This method computes the accuracy as AbstractLearningProblem.getAccuracy(Description), but returns -1 instead of the accuracy if 1.) the accuracy of the description is below the given threshold and 2.) the accuracy of all more special w.r.t. subsumption descriptions is below the given threshold.
double	getAccuracyOrTooWeakApprox(Description description, double noise)
double	getAccuracyOrTooWeakExact(Description description, double noise)
NamedClass	getClassToDescribe()
double	getComissionError()
ClassLearningProblemConfigurator	getConfigurator() For each component, a configurator class is generated in package org.dllearner.core.configurators using the script { org.dllearner.scripts.ConfigJavaGenerator}.
double	getGeneralisedPrecision()
double	getGeneralisedRecall()
double	getInductionRate()
double	getMatchRate()
static String	getName()
double	getOmissionError()
double	getPrecision(Description description)
double	getPredictiveAccuracy()
double	getRecall(Description description)
void	init() Method to be called after the component has been configured.
boolean	isConsistent(Description description)
boolean	isEquivalenceProblem()
static double	p1(int success, int total)
static double	p3(double p1, int total)

Methods inherited from class org.dllearner.core.AbstractLearningProblem

changeReasonerComponent

Methods inherited from class java.lang.Object

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

Constructor Detail

ClassLearningProblem

public ClassLearningProblem(AbstractReasonerComponent reasoner)

Method Detail

getConfigurator

public ClassLearningProblemConfigurator getConfigurator()

Description copied from class: AbstractComponent

For each component, a configurator class is generated in package org.dllearner.core.configurators using the script { org.dllearner.scripts.ConfigJavaGenerator}. The configurator provides set and get methods for the configuration options of a component.

Specified by:

getConfigurator in class AbstractComponent

Returns:

An object allowing to configure this component.

createConfigOptions

public static Collection<ConfigOption<?>> createConfigOptions()

getName

public static String getName()

init

public void init()
          throws ComponentInitException

Description copied from interface: Component

Method to be called after the component has been configured. Implementation of components can overwrite this method to perform setup and initialisation tasks for this component.

Throws:

ComponentInitException - This exception is thrown if any exceptions occur within the initialisation process of this component. As component developer, you are encouraged to rethrow occuring exception as ComponentInitException and giving an error message as well as the actualy exception by using the constructor ComponentInitException.ComponentInitException(String, Throwable).

computeScore

public ClassScore computeScore(Description description)

Description copied from class: AbstractLearningProblem

Computes the Score of a given class description with respect to this learning problem. This can (but does not need to) be used by learning algorithms to measure how good the description fits the learning problem. Score objects are used to store e.g. covered examples, accuracy etc., so often it is more efficient to only create score objects for promising class descriptions.

Specified by:

computeScore in class AbstractLearningProblem

Parameters:

description - A class description (as solution candidate for this learning problem).

Returns:

A Score object.

isEquivalenceProblem

public boolean isEquivalenceProblem()

getAccuracy

public double getAccuracy(Description description)

Description copied from class: AbstractLearningProblem

This method returns a value, which indicates how accurate a class description solves a learning problem. There can be different ways to compute accuracy depending on the type of learning problem and other factors. However, all implementations are required to return a value between 0 and 1, where 1 stands for the highest possible accuracy and 0 for the lowest possible accuracy.

Specified by:

getAccuracy in class AbstractLearningProblem

Returns:

A value between 0 and 1 indicating the quality (of a class description).

getAccuracyOrTooWeak

public double getAccuracyOrTooWeak(Description description,
                                   double noise)

Description copied from class: AbstractLearningProblem

This method computes the accuracy as AbstractLearningProblem.getAccuracy(Description), but returns -1 instead of the accuracy if 1.) the accuracy of the description is below the given threshold and 2.) the accuracy of all more special w.r.t. subsumption descriptions is below the given threshold. This is used for efficiency reasons, i.e. -1 can be returned instantly if it is clear that the description and all its refinements are not sufficiently accurate.

Specified by:

getAccuracyOrTooWeak in class AbstractLearningProblem

Returns:

A value between 0 and 1 indicating the quality (of a class description) or -1 as described above.

getAccuracyOrTooWeakApprox

public double getAccuracyOrTooWeakApprox(Description description,
                                         double noise)

getAccuracyOrTooWeakExact

public double getAccuracyOrTooWeakExact(Description description,
                                        double noise)

getRecall

public double getRecall(Description description)

getPrecision

public double getPrecision(Description description)

getPredictiveAccuracy

public double getPredictiveAccuracy()

getMatchRate

public double getMatchRate()

getOmissionError

public double getOmissionError()

getInductionRate

public double getInductionRate()

getComissionError

public double getComissionError()

getGeneralisedRecall

public double getGeneralisedRecall()

getGeneralisedPrecision

public double getGeneralisedPrecision()

p3

public static double p3(double p1,
                        int total)

p1

public static double p1(int success,
                        int total)

getClassToDescribe

public NamedClass getClassToDescribe()

Returns:

the classToDescribe

evaluate

public EvaluatedDescriptionClass evaluate(Description description)

Description copied from class: AbstractLearningProblem

Evaluates the description by computing the score and returning an evaluated description of the correct type (ClassLearningProblem returns EvaluatedDescriptionClass instead of generic EvaluatedDescription).

Specified by:

evaluate in class AbstractLearningProblem

Parameters:

description - Description to evaluate.

Returns:

isConsistent

public boolean isConsistent(Description description)

Returns:

the isConsistent

followsFromKB

public boolean followsFromKB(Description description)