Modifier and Type | Class and Description |
---|---|
class |
AccMethodAMeasure |
class |
AccMethodAMeasureApprox |
class |
AccMethodFMeasure |
class |
AccMethodFMeasureApprox |
class |
AccMethodFMeasureWeighted |
class |
AccMethodGenFMeasure |
class |
AccMethodJaccard |
class |
AccMethodPredAcc |
class |
AccMethodPredAccApprox |
class |
AccMethodPredAccOCEL |
class |
AccMethodPredAccWeighted |
Modifier and Type | Class and Description |
---|---|
class |
DisjointClassesLearner
Learns disjoint classes using SPARQL queries.
|
class |
NaiveALLearner
Simple example learning algorithm exhaustively creating complex class
expressions of the AL description logic.
|
class |
SimpleSubclassLearner
Learns subclass-relationships for a given class by using SPARQL queries.
|
Modifier and Type | Class and Description |
---|---|
class |
CELOE
The CELOE (Class Expression Learner for Ontology Engineering) algorithm.
|
class |
OEHeuristicRuntime
Search algorithm heuristic for the 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 |
DLTreesRefinementOperator
The original refinement Operator proposed for inducing Terminological Decision Trees
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractTDTClassifier |
class |
TDTClassifier |
Modifier and Type | Class and Description |
---|---|
class |
DisjunctiveHeuristic |
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.
|
class |
StableHeuristic
A stable comparator for search tree nodes.
|
Modifier and Type | Class and Description |
---|---|
class |
FlexibleHeuristic
This heuristic compares two nodes by computing a score
using the number of covered negatives and the horizontal
expansion factor of a node as input.
|
class |
LexicographicHeuristic |
class |
MultiHeuristic
This heuristic combines the following criteria to assign a
double score value to a node:
quality/accuracy of a concept (based on the full training set, not
the negative example coverage as the flexible heuristic)
horizontal expansion
accuracy gain: The heuristic takes into account the accuracy
difference between a node and its parent.
|
class |
OCEL
The DL-Learner learning algorithm component for the example
based refinement operator approach.
|
Modifier and Type | Class and Description |
---|---|
class |
PatternBasedAxiomLearningAlgorithm |
Modifier and Type | Class and Description |
---|---|
class |
EDGEDistibutedSingleStep |
class |
EDGEDistributedDynamic |
Modifier and Type | Class and Description |
---|---|
class |
DummyParameterLearner |
class |
EDGE
This class is a wrapper for EDGE algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
LEAPDistributed |
Modifier and Type | Class and Description |
---|---|
class |
LEAP |
Modifier and Type | Class and Description |
---|---|
class |
AsymmetricObjectPropertyAxiomLearner |
class |
DataPropertyDomainAxiomLearner |
class |
DataPropertyRangeAxiomLearner |
class |
DisjointDataPropertyAxiomLearner |
class |
DisjointObjectPropertyAxiomLearner |
class |
EquivalentDataPropertyAxiomLearner |
class |
EquivalentObjectPropertyAxiomLearner |
class |
FunctionalDataPropertyAxiomLearner |
class |
FunctionalObjectPropertyAxiomLearner |
class |
InverseFunctionalObjectPropertyAxiomLearner |
class |
InverseObjectPropertyAxiomLearner |
class |
IrreflexiveObjectPropertyAxiomLearner |
class |
ObjectPropertyDomainAxiomLearner |
class |
ObjectPropertyRangeAxiomLearner |
class |
ReflexiveObjectPropertyAxiomLearner |
class |
SubDataPropertyOfAxiomLearner |
class |
SubObjectPropertyOfAxiomLearner |
class |
SymmetricObjectPropertyAxiomLearner |
class |
TransitiveObjectPropertyAxiomLearner |
Modifier and Type | Class and Description |
---|---|
class |
QTL2Disjunctive |
class |
QTL2DisjunctiveMultiThreaded
A tree-based algorithm ...
|
Modifier and Type | Class and Description |
---|---|
class |
QueryTreeHeuristicComplex |
class |
QueryTreeHeuristicSimple
A simple heuristic based which just takes the accuracy into account.
|
Modifier and Type | Class and Description |
---|---|
class |
CLI
New commandline interface.
|
class |
ExpressionValidation
Evaluate a class expression on a PosNegLP
|
Modifier and Type | Class and Description |
---|---|
class |
GlobalDoc |
Modifier and Type | Class and Description |
---|---|
class |
OntologyValidation
Evaluate a Probabilistic Knowledge Base (Ontology).
|
Modifier and Type | Interface and Description |
---|---|
interface |
KnowledgeSource
Basic interface for all DL-Learner knowledge sources.
|
Modifier and Type | Class and Description |
---|---|
class |
BUNDLE |
Modifier and Type | Class and Description |
---|---|
class |
KBFile
KB files are an internal convenience format used in DL-Learner.
|
class |
LocalModelBasedSparqlEndpointKS |
class |
OWLFile |
class |
SparqlEndpointKS
SPARQL endpoint knowledge source (without fragment extraction),
in particular for those algorithms which work directly on an endpoint
without requiring an OWL reasoner.
|
Modifier and Type | Class and Description |
---|---|
class |
SparqlKnowledgeSource
Represents the SPARQL Endpoint Component.
|
Modifier and Type | Class and Description |
---|---|
class |
SparqlSimpleExtractor |
Modifier and Type | Class and Description |
---|---|
class |
ClassAsInstanceLearningProblem
A learning problem in which positive and negative examples are classes, i.e.
|
class |
ClassExpressionLearningProblem
The problem of learning the OWL class expression for another OWL class expression
in an OWL ontology.
|
class |
ClassLearningProblem
The problem of learning the OWL class expression of an existing class
in an OWL ontology.
|
class |
ExampleLoader
Load positive and negative examples from Class Expression
|
class |
FuzzyPosNegLPStandard
The aim of this learning problem is to learn a concept definition such that
the positive examples and the negative examples do not follow.
|
class |
PosNegLPStandard
The aim of this learning problem is to learn a concept definition such that
the positive examples and the negative examples do not follow.
|
class |
PosNegLPStrict |
class |
PosNegUndLP
A ternary learning problem (positive, negative and uncertain instances) to manage the problem of the Open World Assumption
typically employed for ontologies
|
class |
PosOnlyLP
A learning problem, where we learn from positive examples only.
|
class |
PropertyAxiomLearningProblem<T extends org.semanticweb.owlapi.model.OWLPropertyAxiom> |
Modifier and Type | Class and Description |
---|---|
class |
ClosedWorldReasoner
Reasoner for fast instance checks.
|
class |
OWLAPIReasoner
Mapping to OWL API reasoner interface.
|
class |
SPARQLReasoner
A reasoner implementation that provides inference services by the execution
of SPARQL queries on
local files (usually in forms of JENA API models)
remote SPARQL endpoints
|
class |
SPARQLReasonerQuad
Specialised SPARQL Reasoner for specific SPARQL dialects
|
Modifier and Type | Class and Description |
---|---|
class |
OperatorInverter
The class uses an existing refinement operator and inverts it, i.e.
|
class |
RhoDRDown
A downward refinement operator, which makes use of domains
and ranges of properties.
|
Modifier and Type | Class and Description |
---|---|
class |
OWLClassExpressionLengthMetric
Generic configurable length metric for class expression length calculation
|
Modifier and Type | Class and Description |
---|---|
class |
MPSemKernelWorkflow
Since the current setup for running a SemKernel example comprises several
steps, like preparing the training data, do the training, preparing the
prediction data and so on, this component is intended to encapsulate this
whole process and make it callable and configurable via the standard
DL-Learner CLI.
|
class |
SemKernelWorkflow
Since the current setup for running a SemKernel example comprises several
steps, like preparing the training data, do the training, preparing the
prediction data and so on, this component is intended to encapsulate this
whole process and make it callable and configurable via the standard
DL-Learner CLI.
|
DL-Learner is licenced under the terms of the GNU General Public License.
Copyright © 2007-2019 Jens Lehmann