Package | Description |
---|---|
org.dllearner.algorithms | |
org.dllearner.algorithms.celoe | |
org.dllearner.algorithms.decisiontrees.dsttdt | |
org.dllearner.algorithms.decisiontrees.tdt | |
org.dllearner.algorithms.el |
Learning algorithms for the EL description logic.
|
org.dllearner.algorithms.meta | |
org.dllearner.algorithms.miles | |
org.dllearner.algorithms.ocel |
New experimental refinement operator approach, which takes
obtained information about concrete examples in an algorithm run
stronger into account.
|
org.dllearner.algorithms.probabilistic.structure.unife.leap | |
org.dllearner.algorithms.qtl |
Learning algorithm based on so-called query trees
which are a tree based representation of a (set of) RDF resource(s)
|
org.dllearner.cli |
DL-Learner command line interface.
|
org.dllearner.core.probabilistic.unife | |
org.dllearner.server |
This package implements the DL-Learner web service.
|
Modifier and Type | Class and Description |
---|---|
class |
NaiveALLearner
Simple example learning algorithm exhaustively creating complex class
expressions of the AL description logic.
|
Modifier and Type | Class and Description |
---|---|
class |
CELOE
The CELOE (Class Expression Learner for Ontology Engineering) algorithm.
|
class |
PCELOE
The PCELOE is an experimental, parallel implementation of the CELOE algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
DSTTDTClassifier |
Modifier and Type | Class and Description |
---|---|
class |
AbstractTDTClassifier |
class |
TDTClassifier |
Modifier and Type | Class and Description |
---|---|
class |
ELLearningAlgorithm
A learning algorithm for EL, which is based on an
ideal refinement operator.
|
class |
ELLearningAlgorithmDisjunctive
A learning algorithm for EL, which will based on an
ideal refinement operator.
|
Modifier and Type | Class and Description |
---|---|
class |
DisjunctiveCELA
A meta algorithm that combines the (partial) solutions of multiple calls of the base class learning algorithm LA
into a disjunction.
|
Constructor and Description |
---|
DisjunctiveCELA(AbstractCELA la) |
Constructor and Description |
---|
MILES(AbstractCELA la,
ClassLearningProblem lp,
AbstractReasonerComponent rc) |
MILES(AbstractCELA la,
PosNegLP lp,
AbstractReasonerComponent rc) |
Modifier and Type | Class and Description |
---|---|
class |
OCEL
The DL-Learner learning algorithm component for the example
based refinement operator approach.
|
Constructor and Description |
---|
ExampleBasedNode(org.semanticweb.owlapi.model.OWLClassExpression concept,
AbstractCELA learningAlgorithm) |
Constructor and Description |
---|
AbstractLEAP(AbstractCELA cela,
AbstractParameterLearningAlgorithm pla) |
LEAP(AbstractCELA cela,
AbstractEDGE lpr) |
Modifier and Type | Class and Description |
---|---|
class |
QTL2Disjunctive |
class |
QTL2DisjunctiveMultiThreaded
A tree-based algorithm ...
|
Modifier and Type | Method and Description |
---|---|
AbstractCELA |
CLI.getLearningAlgorithm() |
Constructor and Description |
---|
CrossValidation(AbstractCELA la,
AbstractClassExpressionLearningProblem lp,
AbstractReasonerComponent rs,
int folds,
boolean leaveOneOut) |
CrossValidation2(AbstractCELA la,
AbstractClassExpressionLearningProblem lp,
AbstractReasonerComponent rs,
int folds,
boolean leaveOneOut) |
Modifier and Type | Method and Description |
---|---|
AbstractCELA |
AbstractPSLA.getClassExpressionLearningAlgorithm() |
Modifier and Type | Method and Description |
---|---|
void |
AbstractPSLA.setClassExpressionLearningAlgorithm(AbstractCELA la) |
Constructor and Description |
---|
AbstractPSLA(AbstractCELA cela,
AbstractParameterLearningAlgorithm pla)
Each probabilistic structure learning algorithm gets a class expression
learning algorithm and a parameter learning algorithm
|
Modifier and Type | Method and Description |
---|---|
AbstractCELA |
ClientState.getLearningAlgorithm() |
Modifier and Type | Method and Description |
---|---|
int |
ClientState.setLearningAlgorithm(AbstractCELA learningAlgorithm) |
DL-Learner is licenced under the terms of the GNU General Public License.
Copyright © 2007-2019 Jens Lehmann