# DL-Learner Components

Click on the following items to filter the listing below by implemented interfaces (requires Javascript):

show all

- Non-component Class
- KnowledgeSource
- LearningAlgorithm
  - AxiomLearningAlgorithm
  - ClassExpressionLearningAlgorithm
- LearningProblem
- ReasonerComponent
- RefinementOperator
- Heuristic
- AccMethod
- other

Click on a component to get an overview on its configuration options.

## AMeasure org.dllearner.accuracymethods.AccMethodAMeasure

short name
  ameasure
version
  0.1
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
beta | beta factor (0 = do not use) | double | 0 | false

## AMeasure Approximate org.dllearner.accuracymethods.AccMethodAMeasureApprox

short name
  approx.ameasure
version
  0.1
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
approxDelta | The Approximate Delta | double | 0.05 | false
beta | beta factor (0 = do not use) | double | 0 | false
reasoner | (configured by learning problem) | Reasoner |  | false

## BUNDLE org.dllearner.core.probabilistic.unife.BUNDLE

short name
  bundle
version
  1.0
implements
  - OtherComponent

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracy | accuracy used during the computation of the probabilistic values (number of digital places) | int | 5 | false
bddFType | library used for BDD compilation | String | buddy | false
maxExplanations | the maximum number of explanations to find for each query | int | 2147483647 | false
timeout | max time allowed for the inference (format: [0-9]h[0-9]m[0-9]s) | String | 0s (infinite timeout) | false

## CELOE org.dllearner.algorithms.celoe.CELOE

short name
  celoe
version
  1.0
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm
description
  CELOE is an adapted and extended version of the OCEL algorithm applied for the ontology engineering use case. See http://jens-lehmann.org/files/2011/celoe.pdf for reference.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
expandAccuracy100Nodes | whether to try and refine solutions which already have accuracy value of 1 | boolean | false | false
filterDescriptionsFollowingFromKB | If true, then the results will not contain suggestions, which already follow logically from the knowledge base. Be careful, since this requires a potentially expensive consistency check for candidate solutions. | boolean | false | false
heuristic | no description available | AbstractHeuristic | celoe_heuristic | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxClassExpressionTests | The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.) | int | 0 | false
maxClassExpressionTestsAfterImprovement | The maximum number of candidate hypothesis the algorithm is allowed after an improvement in accuracy (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.) | int | 0 | false
maxDepth | maximum depth of description | double | 7 | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
maxExecutionTimeInSecondsAfterImprovement | maximum execution of the algorithm in seconds after last improvement | int | 0 | false
maxNrOfResults | Sets the maximum number of results one is interested in. (Setting this to a lower value may increase performance as the learning algorithm has to store/evaluate/beautify less descriptions). | int | 10 | false
noisePercentage | the (approximated) percentage of noise within the examples | double | 0.0 | false
operator | the refinement operator instance to use | LengthLimitedRefinementOperator |  | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
replaceSearchTree | specifies whether to replace the search tree in the log file after each run or append the new search tree | boolean | false | false
reuseExistingDescription | If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base. | boolean | false | false
searchTreeFile | file to use for the search tree | String | log/searchTree.txt | false
singleSuggestionMode | Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples. | boolean | false | false
startClass | You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax either with full IRIs or prefixed IRIs. Example: ex:Male or http://example.org/ontology/Female | OWLClassExpression | owl:Thing | false
stopOnFirstDefinition | algorithm will terminate immediately when a correct definition is found | boolean | false | false
terminateOnNoiseReached | specifies whether to terminate when noise criterion is met | boolean | false | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false
writeSearchTree | specifies whether to write a search tree | boolean | false | false

## Class as Instance LP org.dllearner.learningproblems.ClassAsInstanceLearningProblem

short name
  classasinstance
version
  0.1
implements
  - LearningProblem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
exampleLoaderHelper | load examples via class expression selector | ExampleLoader |  | false
negativeExamples | no description available | Set |  | false
percentPerLengthUnit | Percent Per Length Unit | double | 0.05 | false
positiveExamples | no description available | Set |  | false

## ClassExpressionLearningProblem org.dllearner.learningproblems.ClassExpressionLearningProblem

short name
  celp
version
  0.6
implements
  - LearningProblem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracyMethod | Specifies, which method/function to use for computing accuracy. Available measues are "pred_acc" (predictive accuracy), "fmeasure" (F measure), "generalised_fmeasure" (generalised F-Measure according to Fanizzi and d'Amato). | AccMethod | PRED_ACC | false
betaEq | beta index for F-measure in definition learning | double | 1.0 | false
betaSC | beta index for F-measure in super class learning | double | 3.0 | false
checkConsistency | whether to check for consistency of suggestions (when added to ontology) | boolean | true | false
classExpressionToDescribe | OWL class expression of which an OWL class expression should be learned | OWLClassExpression |  | true
equivalence | Whether this is an equivalence problem (or superclass learning problem) | boolean | true | false
exampleLoaderHelper | load examples via class expression selector | ExampleLoader |  | false
maxExecutionTimeInSeconds | Maximum execution time in seconds | int | 10 | false

## ClassLearningProblem org.dllearner.learningproblems.ClassLearningProblem

short name
  clp
version
  0.6
implements
  - LearningProblem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracyMethod | Specifies, which method/function to use for computing accuracy. Available measues are "pred_acc" (predictive accuracy), "fmeasure" (F measure), "generalised_fmeasure" (generalised F-Measure according to Fanizzi and d'Amato). | AccMethod | PRED_ACC | false
betaEq | beta index for F-measure in definition learning | double | 1.0 | false
betaSC | beta index for F-measure in super class learning | double | 3.0 | false
checkConsistency | whether to check for consistency of suggestions (when added to ontology) | boolean | true | false
classToDescribe | class of which an OWL class expression should be learned | IRI |  | true
equivalence | Whether this is an equivalence problem (or superclass learning problem) | boolean | true | false
exampleLoaderHelper | load examples via class expression selector | ExampleLoader |  | false
maxExecutionTimeInSeconds | Maximum execution time in seconds | int | 10 | false

## Command Line Interface org.dllearner.cli.CLI

option name | description | type | default value | required?
--- | --- | --- | --- | ---
logLevel | Configure logger log level from conf file. Available levels: "FATAL", "ERROR", "WARN", "INFO", "DEBUG", "TRACE". Note, to see results, at least "INFO" is required. | String | INFO | false
nrOfFolds | Number of folds in Cross-Validation mode | int | 10 | false
performCrossValidation | Run in Cross-Validation mode | boolean | false | false

## Disjunctive ELTL org.dllearner.algorithms.el.ELLearningAlgorithmDisjunctive

short name
  deltl
version
  0.5
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm
description
  Disjunctive ELTL is an algorithm based on the refinement operator in http://jens-lehmann.org/files/2009/el_ilp.pdf with support for disjunctions.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
instanceBasedDisjoints | whether to do real disjoint tests or check that two named classes do not have common instances | boolean |  | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
minimumTreeScore | the minimum quality a tree must have to proceed | double | -1 | false
noisePercentage | the (approximated) percentage of noise within the examples | double | 0.0 | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
startClass | You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes. | OWLClassExpression | owl:Thing | false
stopOnFirstDefinition | algorithm will terminate immediately when a correct definition is found | boolean | false | false
treeSearchTimeSeconds | Specifies how long the algorithm should search for a partial solution (a tree). | double | 1.0 | false
tryFullCoverage | If yes, then the algorithm tries to cover all positive examples. Note that while this improves accuracy on the testing set, it may lead to overfitting. | boolean | false | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false

## DisjunctiveHeuristic org.dllearner.algorithms.el.DisjunctiveHeuristic

short name
  disjunctive_heuristic
version
  0.1
implements
  - Heuristic

This component does not have configuration options.

## DummyParameterLearner org.dllearner.algorithms.probabilistic.parameter.unife.edge.DummyParameterLearner

short name
  dummypl
version
  1.0
implements
  - LearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracy | accuracy used during the computation of the probabilistic values (number of digital places) | int | 5 | false
differenceLL | stop difference between log-likelihood of two consecutive EM cycles | double | 0.000000000028 | false
fixedProbability | Value of the fixed probability. All the probabilistic axioms will have the same probability | double | 0.4 | false
keepParameters | If true EDGE keeps the old parameter values of all the probabilistic axioms and it does not relearn them | boolean | false | false
maxExplanations | the maximum number of explanations to find for each query | int | 2147483647 | false
maxIterations | maximum number of cycles | long | 9223372036854775807 | false
maxNegativeExamples | max number of negative examples that edge must handle when a class learning problem is given | int | 0 (infinite) | false
maxPositiveExamples | max number of positive examples that edge must handle when a class learning problem is given | int | 0 (infinite) | false
outputformat | format of the output file | PossibleOutputFormat | OWLXML | false
probabilizeAll | make probabilistic all the axioms in the starting probabilistic ontology (including non probabilistic ones) | boolean | false | false
randomize | randomize the starting probabilities of the probabilistic axioms | boolean | false | false
ratioLL | stop ratio between log-likelihood of two consecutive EM cycles | double | 0.000000000028 | false
seed | seed for random generation | int | 0 | false
showAll | force the visualization of all results | boolean | false | false
timeout | max time allowed for the inference (format: [0-9]h[0-9]m[0-9]s) | String | 0s (infinite timeout) | false

## EDGE org.dllearner.algorithms.probabilistic.parameter.unife.edge.EDGE

short name
  edge
version
  1.0
implements
  - LearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracy | accuracy used during the computation of the probabilistic values (number of digital places) | int | 5 | false
differenceLL | stop difference between log-likelihood of two consecutive EM cycles | double | 0.000000000028 | false
keepParameters | If true EDGE keeps the old parameter values of all the probabilistic axioms and it does not relearn them | boolean | false | false
maxExplanations | the maximum number of explanations to find for each query | int | 2147483647 | false
maxIterations | maximum number of cycles | long | 9223372036854775807 | false
maxNegativeExamples | max number of negative examples that edge must handle when a class learning problem is given | int | 0 (infinite) | false
maxPositiveExamples | max number of positive examples that edge must handle when a class learning problem is given | int | 0 (infinite) | false
outputformat | format of the output file | PossibleOutputFormat | OWLXML | false
probabilizeAll | make probabilistic all the axioms in the starting probabilistic ontology (including non probabilistic ones) | boolean | false | false
randomize | randomize the starting probabilities of the probabilistic axioms | boolean | false | false
ratioLL | stop ratio between log-likelihood of two consecutive EM cycles | double | 0.000000000028 | false
seed | seed for random generation | int | 0 | false
showAll | force the visualization of all results | boolean | false | false
timeout | max time allowed for the inference (format: [0-9]h[0-9]m[0-9]s) | String | 0s (infinite timeout) | false

## EDGEDistributedDynamic org.dllearner.algorithms.probabilistic.parameter.distributed.unife.edge.EDGEDistributedDynamic

short name
  edgedynamic
version
  1.0
implements
  - LearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
chunkDim | number of example for chunk | int | 1 | false
maxSenderThreads | max number of concurrent threads which send examples to the slaves | int | #processors - 1 | false

## EDGEDistributedSingleStep org.dllearner.algorithms.probabilistic.parameter.distributed.unife.edge.EDGEDistibutedSingleStep

short name
  edgesingle
version
  1.0
implements
  - LearningAlgorithm

This component does not have configuration options.

## ELTL org.dllearner.algorithms.el.ELLearningAlgorithm

short name
  eltl
version
  0.5
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm
description
  ELTL is an algorithm based on the refinement operator in http://jens-lehmann.org/files/2009/el_ilp.pdf.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
classToDescribe | class of which an OWL class expression should be learned | IRI |  | false
heuristic | The heuristic variable to use for ELTL | ELHeuristic | StableHeuristic | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
instanceBasedDisjoints | Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator. | boolean | true | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxClassExpressionDepth | The maximum depth for class expressions to test | int | 2 | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
maxNrOfResults | Sets the maximum number of results one is interested in | int | 10 | false
noisePercentage | the (approximated) percentage of noise within the examples | double | 0.0 | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
replaceSearchTree | specifies whether to replace the search tree in the log file after each run or append the new search tree | boolean | false | false
searchTreeFile | file to use for the search tree | String | log/searchTree.txt | false
startClass | You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes. | OWLClassExpression | owl:Thing | false
stopOnFirstDefinition | algorithm will terminate immediately when a correct definition is found | boolean | false | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false
writeSearchTree | specifies whether to write a search tree | boolean | false | false

## ETDT org.dllearner.algorithms.decisiontrees.dsttdt.DSTTDTClassifier

short name
  etdt
version
  1.0
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm
description
  An Evidence-based Terminological Decision Tree

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
beam | value for limiting the number of generated concepts | int | 4 | false
heuristic | instance of heuristic to use | TreeInductionHeuristics | TreeInductionHeuristics | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
nonSpecifityControl | a flag to decide if further control on the purity measure should be made | boolean | false | false
operator | refinement operator instance to use | RefinementOperator | DLTreesRefinementOperator | false
puritythreshold | Purity threshold for setting a leaf | double | 0.05 | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false

## ExampleLoader org.dllearner.learningproblems.ExampleLoader

short name
  ExampleLoader
version
  0.1
implements
  - OtherComponent
description
  Load examples from Class Expression

option name | description | type | default value | required?
--- | --- | --- | --- | ---
negativeExamplesCE | class expression of negative examples | OWLClassExpression |  | false
negativeRandomCount | randomly choose only so many negative examples | int |  | false
positiveExamplesCE | class expression of positive examples | OWLClassExpression |  | false
positiveRandomCount | randomly choose only so many positive examples | int |  | false
randomSeed | random seed for deterministic example choice | long |  | false

## FMeasure org.dllearner.accuracymethods.AccMethodFMeasure

short name
  fmeasure
version
  0.0
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
beta | beta factor (0 = do not use) | double | 0 | false

## FMeasure Approximate org.dllearner.accuracymethods.AccMethodFMeasureApprox

short name
  approx.fmeasure
version
  0.0
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
approxDelta | The Approximate Delta | double | 0.05 | false
beta | beta factor (0 = do not use) | double | 0 | false
reasoner | reasoner component (configured by learning problem) | Reasoner |  | false

## Flexible Heuristic org.dllearner.algorithms.ocel.FlexibleHeuristic

short name
  flexheuristic
version
  0.1
implements
  - Heuristic

option name | description | type | default value | required?
--- | --- | --- | --- | ---
nrOfNegativeExamples | the number of negative examples | int |  | false
percentPerLengthUnit | score percent to deduct per expression length | double |  | true

## FuzzyPosNegLPStandard org.dllearner.learningproblems.FuzzyPosNegLPStandard

short name
  fuzzyPosNeg
version
  0.2
implements
  - LearningProblem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracyMethod | Specifies, which method/function to use for computing accuracy. Available measues are "PRED_ACC" (predictive accuracy), "FMEASURE" (F measure), "GEN_FMEASURE" (generalised F-Measure according to Fanizzi and d'Amato). | HeuristicType | PRED_ACC | false
fuzzyExamples | no description available | SortedSet |  | false
negativeExamples | no description available | SortedSet |  | false
positiveExamples | no description available | SortedSet |  | false

## GLOBAL OPTIONS org.dllearner.cli.DocumentationGeneratorMeta.GlobalDoc

option name | description | type | default value | required?
--- | --- | --- | --- | ---
prefixes | Mapping of prefixes to replace inside other configuration file entries Example: [ ("ex","http://example.com/father#") ] | Map |  | false
rendering | The string renderer for any OWL expression output, can be "dlsyntax" or "manchester" Example: dlsyntax | String | manchester | false

## Generalised FMeasure org.dllearner.accuracymethods.AccMethodGenFMeasure

short name
  gen_fmeasure
version
  0.1
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
beta | beta factor (0 = do not use) | double | 0 | false

## Jaccard Coefficient org.dllearner.accuracymethods.AccMethodJaccard

short name
  jaccard
version
  0.1
implements
  - AccMethod

This component does not have configuration options.

## KB File org.dllearner.kb.KBFile

short name
  kbfile
version
  0.8
implements
  - KnowledgeSource

option name | description | type | default value | required?
--- | --- | --- | --- | ---
baseDir | change the base directory (must be absolute) | String | directory of conf file | false
fileName | relative or absolute path to KB file | String |  | false
url | URL pointer to the KB file | String |  | false

## LEAP org.dllearner.algorithms.probabilistic.structure.unife.leap.LEAP

short name
  leap
version
  1.0
implements
  - LearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracy | accuracy used during the computation of the probabilistic values (number of digital places) | int | 5 | false
blockSizeGreedySearch | the number of probabilistic axioms that LEAP tries to add into the ontology at each iteration of the greedy search | int | 1 | false
classAxiomType | This is used to set the type of class axiom to learn. Accepted values (case insensitive): 'subClassOf', 'equivalentClasses', 'both' | String | subClassOf | false
dummyClass | You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes. | IRI | owl:learnedClass | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | int | 10 | false

## LEAPDistributed org.dllearner.algorithms.probabilistic.structure.distributed.unife.leap.LEAPDistributed

short name
  leapdistr
version
  1.0
implements
  - LearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracy | accuracy used during the computation of the probabilistic values (number of digital places) | int | 5 | false
differenceLL | stop difference between log-likelihood of two consecutive iterations | BigDecimal | 0.00001 | false
dummyClass | You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes. | IRI | owl:learnedClass | false
maxIterations | maximum number of iterations | long | 2147000000 | false
procPLA | number of mpi processes of parameter learning algorithm for each probabilistic structure learner process | int | 1 | false
procPSLA | number of mpi processes of probabilistic structure learning algorithm | int | 1 | false
ratioLL | stop ratio between log-likelihood of two consecutive iterations | BigDecimal | 0.00001 | false
targetAxiomsFilename | probabilistic target axioms which can be deleted from the ontology | String |  | false

## Lexicographic Heuristic org.dllearner.algorithms.ocel.LexicographicHeuristic

short name
  lexheuristic
version
  0.1
implements
  - Heuristic

This component does not have configuration options.

## Local Endpoint org.dllearner.kb.LocalModelBasedSparqlEndpointKS

short name
  local_sparql
version
  0.9
implements
  - KnowledgeSource

option name | description | type | default value | required?
--- | --- | --- | --- | ---
cacheDir | The base directory of the SPARQL query cache. | String | tmp folder of the system | false
cacheTTL | The time to live in milliseconds for cached SPARQL queries, if enabled. The default value is 86400s(=1 day). | long | 86400 | false
defaultGraphURIs | a list of default graph URIs | List | {} | false
namedGraphURIs | a list of named graph URIs | List | {} | false
pageSize | page size Example: 10000 | long | 10 000 | false
queryDelay | Use this setting to avoid overloading the endpoint with a sudden burst of queries. A value below 0 means no delay. | long | 50 | false
retryCount | The maximum number of retries for the execution of a particular SPARQL query. | int | 3 | false
url | URL of the SPARQL endpoint | URL |  | true
useCache | Use this setting to enable caching of SPARQL queries in a local database. | boolean | true | false

## Mammalian Phenotype SemKernel Workflow org.dllearner.utilities.semkernel.MPSemKernelWorkflow

short name
  mpskw
version
  0.1
implements
  - OtherComponent

This component does not have configuration options.

## Naive AL Learner org.dllearner.algorithms.NaiveALLearner

short name
  naiveALLearner
version
  0.1
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
maxLength | maximum length of class expression | int | 4 | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false

## OEHeuristicRuntime org.dllearner.algorithms.celoe.OEHeuristicRuntime

short name
  celoe_heuristic
version
  0.5
implements
  - Heuristic

option name | description | type | default value | required?
--- | --- | --- | --- | ---
expansionPenaltyFactor | penalty for long descriptions (horizontal expansion) (strong by default) | double | 0.1 | false
gainBonusFactor | bonus for being better than parent node | double | 0.3 | false
nodeRefinementPenalty | penalty if a node description has very many refinements since exploring such a node is computationally very expensive | double | 0.0001 | false
startNodeBonus | no description available | double | 0.1 | false

## OWL API Reasoner org.dllearner.reasoning.OWLAPIReasoner

short name
  oar
version
  0.8
implements
  - ReasonerComponent

option name | description | type | default value | required?
--- | --- | --- | --- | ---
owlLinkURL | specifies the URL of the remote OWLLink server | String | null | false
precomputeClassHierarchy | if class hierarchy should be precomputed | boolean | true | false
precomputeDataPropertyHierarchy | no description available | boolean | true | false
precomputeObjectPropertyHierarchy | no description available | boolean | true | false
precomputeObjectPropertyRanges | if object property ranges should be precomputed | boolean | true | false
precomputePropertyDomains | if property domains should be precomputed | boolean | true | false
reasonerImplementation | specifies the used OWL API reasoner implementation | ReasonerImplementation | pellet | false
sources | the underlying knowledge sources | Set |  | true
useFallbackReasoner | specifies whether to use a fallback reasoner if a reasoner call fails because it's not supported or results in a bug. (the fallback works only on the assertional level | boolean | false | false
useInstanceChecks | whether to use single instance checks | boolean | false | false

## OWL Class Expression Learner org.dllearner.algorithms.ocel.OCEL

short name
  ocel
version
  1.2
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
candidatePostReductionSize | maximum number of candidates to retain | int | 30 | false
computeBenchmarkInformation | specifies whether to compute and log benchmark information | boolean | false | false
expansionPenaltyFactor | For the MultiHeuristic: how much accuracy gain is worth an increase of horizontal expansion by one (typical value: 0.01) | double | 0.02 | false
forceRefinementLengthIncrease | if this variable is set to true, then the refinement operator is applied until all concept of equal length have been found e.g. TOP -> A1 -> A2 -> A3 is found in one loop; the disadvantage are potentially more method calls, but the advantage is that the algorithm is better in locating relevant concept in the subsumption hierarchy (otherwise, if the most general concept is not promising, it may never get expanded) | boolean | true | false
guaranteeXgoodDescriptions | how many sufficient solutions must be found before termination, if terminateOnNoiseReached is enabled | int | 1 | false
heuristic | the heuristic to guide the search | ExampleBasedHeuristic | MultiHeuristic | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
improveSubsumptionHierarchy | if enabled, modifies the subsumption hierarchy such that for each class, there is only a single path to reach it via upward and downward refinement respectively. | boolean | true | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
lengthMetric | adjust the weights of class expression length in refinement | OWLClassExpressionLengthMetric | OCEL default metric | false
maxClassDescriptionTests | The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit). The algorithm will stop afterwards | int | 0 | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
minExecutionTimeInSeconds | Minimum time the algorithm has to run before termination (even if solution already found | int | 0 | false
negationPenalty | (for the ExampleBasedNode.) penalty value to deduce for using a negated class expression (complementOf) | int | 0 | false
negativeWeight | (for the ExampleBasedNode.) weighting factor on the number of true negatives (true positives are weigthed with 1) | double | 1.0 | false
noisePercentage | noise regulates how many positives can be misclassified and when the algorithm terminates | double | 0.0 | false
operator | the refinement operator instance to use | LengthLimitedRefinementOperator | RhoDRDown | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
replaceSearchTree | specifies whether to replace the search tree in the log file after each run or append the new search tree | boolean | false | false
searchTreeFile | file to use for the search tree | File | log/searchTree.txt | false
showBenchmarkInformation | show additional timing info for benchmark purposes | boolean | false | false
startClass | You can specify a start class for the algorithm Example: ex:Male or http://example.org/ontology/Female | OWLClassExpression | owl:Thing | false
startNodeBonus | (for the ExampleBasedNode.) the score value for the start node | double | 0.1 | false
terminateOnNoiseReached | specifies whether to terminate when noise criterion is met | boolean | true | false
useCandidateReduction | candidate reduction: using this mechanism we can simulate the divide&conquer approach in many ILP programs using a clause by clause search; after a period of time the candidate set is reduced to focus CPU time on the most promising concepts | boolean | true | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false
useOverlyGeneralList | no description available | boolean | true | false
usePropernessChecks | if set to false we do not test properness; this may seem wrong but the disadvantage of properness testing are additional reasoner queries and a search bias towards ALL r.something because ALL r.TOP is improper and automatically expanded further | boolean | false | false
useShortConceptConstruction | whether to shorten concepts to ignore identical refinement. e.g. male AND male is shortened to male. | boolean | true | false
useTooWeakList | exclude too weak concepts when they occur as sub concept | boolean | true | false
useTreeTraversal | tree traversal means to run through the most promising concepts and connect them in an intersection to find a solution (this is called irregularly e.g. every 100 seconds) | boolean | false | false
writeSearchTree | specifies whether to write a search tree | boolean | false | false

## OWL Class Expression Length Metric org.dllearner.utilities.owl.OWLClassExpressionLengthMetric

short name
  cel_metric
version
  0.1
implements
  - OtherComponent

option name | description | type | default value | required?
--- | --- | --- | --- | ---
classLength | Class: "C" | int | 1 | false
dataAllValuesLength | Data All Values: "∀" p.t | int | 1 | false
dataCardinalityLength | Data Cardinality restriction: "≤n" r.t | int | 2 | false
dataComplementLength | Data Complement: "¬"datatype | int | 1 | false
dataHasValueLength | Data Has Value: "∃" p."{V}" | int | 2 | false
dataIntersectionLength | Data Intersection: datatype"⨅"datatype | int | 1 | false
dataOneOfLength | Data One of: ∃ p."{U,V,W}" | int | 1 | false
dataProperyLength | Data Property: ∃ "p".t | int | 1 | false
dataSomeValuesLength | Data Some Values: "∃" p.t | int | 1 | false
dataUnionLength | Data Union: datatype"⨆"datatype | int | 1 | false
datatypeLength | Datatype: "^^datatype" | int | 1 | false
objectAllValuesLength | Obj. All Values: "∀" r.C | int | 1 | false
objectCardinalityLength | Obj. Cardinality restriction: "≤n" r.C | int | 2 | false
objectComplementLength | Complement: "¬"C | int | 1 | false
objectHasSelfLength | Obj. Self restriction: "∃" r.Self | int | 2 | false
objectHasValueLength | Obj. Has Value: "∃" r."{I}" | int | 2 | false
objectIntersectionLength | Intersection: A"⨅"B | int | 1 | false
objectInverseLength | Inverse property: ∃ "r⁻".C | int | 2 | false
objectOneOfLength | Obj. One of: ∃ r."{X,Y,Z}" | int | 1 | false
objectProperyLength | Obj. Property: ∃ "r".C | int | 1 | false
objectSomeValuesLength | Obj. Some Values: "∃" r.C | int | 1 | false
objectUnionLength | Union: A"⨆"B | int | 1 | false

## OWL File org.dllearner.kb.OWLFile

short name
  owlfile
version
  0.9
implements
  - KnowledgeSource

option name | description | type | default value | required?
--- | --- | --- | --- | ---
baseDir | separately specify directory of KB file | String |  | false
defaultGraphURIs | a list of default graph URIs to query from the Endpoint | List |  | false
fileName | relative or absolute path to KB file | String |  | false
namedGraphURIs | a list of named graph URIs to query from the Endpoint | List |  | false
reasoningString | Enable JENA reasoning on the Ontology Model. Available reasoners are: "micro_rule", "mini_rule", "rdfs", "rule" | String | false | false
sparql | SPARQL CONSTRUCT expression to download from Endpoint | String |  | false
url | URL pointer to the KB file or Endpoint | URL |  | false

## OperatorInverter org.dllearner.refinementoperators.OperatorInverter

short name
  inv_op
version
  0.1
implements
  - RefinementOperator

option name | description | type | default value | required?
--- | --- | --- | --- | ---
guaranteeLength | Whether inverse solutions must respect the desired max length | boolean | true | false
lengthMetric | class expression length calculation metric | OWLClassExpressionLengthMetric |  | false
operator | operator to invert | LengthLimitedRefinementOperator |  | true
useNegationNormalForm | whether to apply NNF | boolean | true | false

## PCELOE org.dllearner.algorithms.celoe.PCELOE

short name
  pceloe
version
  1.0
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm
description
  PCELOE is an experimental, parallel implementation of the CELOE algorithm.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
expandAccuracy100Nodes | whether to try and refine solutions which already have accuracy value of 1 | boolean | false | false
filterDescriptionsFollowingFromKB | If true, then the results will not contain suggestions, which already follow logically from the knowledge base. Be careful, since this requires a potentially expensive consistency check for candidate solutions. | boolean | false | false
heuristic | no description available | AbstractHeuristic | celoe_heuristic | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxClassExpressionTests | The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.) | int | 0 | false
maxClassExpressionTestsAfterImprovement | The maximum number of candidate hypothesis the algorithm is allowed after an improvement in accuracy (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.) | int | 0 | false
maxDepth | maximum depth of description | double | 7 | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
maxExecutionTimeInSecondsAfterImprovement | maximum execution of the algorithm in seconds | int | 0 | false
maxNrOfResults | Sets the maximum number of results one is interested in. (Setting this to a lower value may increase performance as the learning algorithm has to store/evaluate/beautify less descriptions). | int | 10 | false
noisePercentage | the (approximated) percentage of noise within the examples | double | 0.0 | false
nrOfThreads | number of threads running in parallel | int | 2 | false
operator | the refinement operator instance to use | LengthLimitedRefinementOperator |  | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
replaceSearchTree | specifies whether to replace the search tree in the log file after each run or append the new search tree | boolean | false | false
reuseExistingDescription | If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base. | boolean | false | false
searchTreeFile | file to use for the search tree | String | log/searchTree.txt | false
singleSuggestionMode | Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples. | boolean | false | false
startClass | You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes. | OWLClassExpression | owl:Thing | false
stopOnFirstDefinition | algorithm will terminate immediately when a correct definition is found | boolean | false | false
terminateOnNoiseReached | specifies whether to terminate when noise criterion is met | boolean | false | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false
writeSearchTree | specifies whether to write a search tree | boolean | false | false

## PosNegLPStandard org.dllearner.learningproblems.PosNegLPStandard

short name
  posNegStandard
version
  0.8
implements
  - LearningProblem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracyMethod | Specifies, which method/function to use for computing accuracy. Available measues are "PRED_ACC" (predictive accuracy), "FMEASURE" (F measure), "GEN_FMEASURE" (generalised F-Measure according to Fanizzi and d'Amato). | AccMethodTwoValued | PRED_ACC | false
negativeExamples | list of negative examples | Set |  | true
percentPerLengthUnit | Percent Per Length Unit | double | 0.05 | false
positiveExamples | list of positive examples | Set |  | true
useRetrievalForClassification | "Specifies whether to use retrieval or instance checks for testing a concept. - NO LONGER FULLY SUPPORTED. | boolean | false | false

## PosNegLPStrict org.dllearner.learningproblems.PosNegLPStrict

short name
  posNegStrict
version
  0.1
implements
  - LearningProblem
description
  three valued definition learning problem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
accuracyMethod | Specifies, which method/function to use for computing accuracy. Available measues are "PRED_ACC" (predictive accuracy), "FMEASURE" (F measure), "GEN_FMEASURE" (generalised F-Measure according to Fanizzi and d'Amato). | AccMethodTwoValued | PRED_ACC | false
accuracyPenalty | penalty for pos/neg examples which are classified as neutral | double | 1.0 | false
errorPenalty | penalty for pos. examples classified as negative or vice versa | double | 3.0 | false
negativeExamples | list of negative examples | Set |  | true
penaliseNeutralExamples | if set to true neutral examples are penalised | boolean |  | false
percentPerLengthUnit | Percent Per Length Unit | double | 0.05 | false
positiveExamples | list of positive examples | Set |  | true
useRetrievalForClassification | "Specifies whether to use retrieval or instance checks for testing a concept. - NO LONGER FULLY SUPPORTED. | boolean | false | false

## PosNegUndLP org.dllearner.learningproblems.PosNegUndLP

short name
  posNegUndLP
version
  1.0
implements
  - LearningProblem
description
  A learning problem with uncertain-membership instances

option name | description | type | default value | required?
--- | --- | --- | --- | ---
uncertainExamples | the uncertain examples | Set |  | true

## Predictive Accuracy org.dllearner.accuracymethods.AccMethodPredAcc

short name
  pred_acc
version
  0.0
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
beta | beta factor (0 = do not use) | double | 0 | false

## Predictive Accuracy Approximate org.dllearner.accuracymethods.AccMethodPredAccApprox

short name
  approx.prec_acc
version
  0.0
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
approxDelta | The Approximate Delta | double | 0.05 | false
beta | beta factor (0 = do not use) | double | 0 | false
reasoner | (configured by the learning problem) | Reasoner |  | false

## Predictive Accuracy without Weak elimination org.dllearner.accuracymethods.AccMethodPredAccOCEL

short name
  pred_acc.ocel
version
  0.0
implements
  - AccMethod

This component does not have configuration options.

## PropertyAxiomLearningProblem org.dllearner.learningproblems.PropertyAxiomLearningProblem

short name
  palp
version
  0.6
implements
  - LearningProblem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
reasoner | The reasoner component variable to use for this Learning Problem | AbstractReasonerComponent |  | false

## QueryTreeHeuristic org.dllearner.algorithms.qtl.heuristics.QueryTreeHeuristicSimple

short name
  qtree_heuristic_simple
version
  0.1
implements
  - Heuristic

This component does not have configuration options.

## QueryTreeHeuristicC org.dllearner.algorithms.qtl.heuristics.QueryTreeHeuristicComplex

short name
  qtree_heuristic_complex
version
  0.1
implements
  - Heuristic

This component does not have configuration options.

## Refinement Operator TDT org.dllearner.algorithms.decisiontrees.refinementoperators.DLTreesRefinementOperator

short name
  tdtop
version
  1.0
implements
  - RefinementOperator

option name | description | type | default value | required?
--- | --- | --- | --- | ---
beam | no description available | int | 5 | false
lp | the learning problem instance to use | PosNegLP |  | false
reasoner | the reasoner instance to use | Reasoner |  | false
ro | no description available | int | 1 | false

## SPARQL Reasoner org.dllearner.reasoning.SPARQLReasoner

short name
  spr
version
  0.1
implements
  - ReasonerComponent

option name | description | type | default value | required?
--- | --- | --- | --- | ---
laxMode | Use alternative relaxed Sparql-queries for Classes and Individuals | boolean | false | false
precomputeClassHierarchy | if class hierarchy should be precomputed | boolean | true | false
precomputeDataPropertyHierarchy | no description available | boolean | true | false
precomputeObjectPropertyHierarchy | no description available | boolean | true | false
precomputeObjectPropertyRanges | if object property ranges should be precomputed | boolean | true | false
precomputePropertyDomains | if property domains should be precomputed | boolean | true | false
preferAsk | Prefer ASK queries when there is a choice in implementation | boolean | true | false
sources | the underlying knowledge sources | Set |  | true
useGenericSplitsCode | Whether to use the generic facet generation code, which requires downloading all instances and is thus not recommended | boolean | false | false
useInstanceChecks | whether to use single instance checks | boolean | false | false
useValueLists | Whether to use SPARQL1.1 Value Lists | boolean | false | false

## SPARQL Reasoner (Quad) org.dllearner.reasoning.SPARQLReasonerQuad

short name
  spr.quad
version
  0.1
implements
  - ReasonerComponent

option name | description | type | default value | required?
--- | --- | --- | --- | ---
laxMode | Use alternative relaxed Sparql-queries for Classes and Individuals | boolean | false | false
preferAsk | Prefer ASK queries when there is a choice in implementation | boolean | true | false
useGenericSplitsCode | Whether to use the generic facet generation code, which requires downloading all instances and is thus not recommended | boolean | false | false
useValueLists | Whether to use SPARQL1.1 Value Lists | boolean | false | false

## SPARQL endpoint org.dllearner.kb.SparqlEndpointKS

short name
  sparql
version
  0.2
implements
  - KnowledgeSource

option name | description | type | default value | required?
--- | --- | --- | --- | ---
cacheDir | The base directory of the SPARQL query cache. | String | tmp folder of the system | false
cacheTTL | The time to live in milliseconds for cached SPARQL queries, if enabled. The default value is 86400s(=1 day). | long | 86400 | false
defaultGraphURIs | a list of default graph URIs | List | {} | false
namedGraphURIs | a list of named graph URIs | List | {} | false
pageSize | page size Example: 10000 | long | 10 000 | false
queryDelay | Use this setting to avoid overloading the endpoint with a sudden burst of queries. A value below 0 means no delay. | long | 50 | false
retryCount | The maximum number of retries for the execution of a particular SPARQL query. | int | 3 | false
url | URL of the SPARQL endpoint | URL |  | true
useCache | Use this setting to enable caching of SPARQL queries in a local database. | boolean | true | false

## SPARQL endpoint fragment org.dllearner.kb.sparql.SparqlKnowledgeSource

short name
  sparqlfrag
version
  0.5
implements
  - KnowledgeSource

This component does not have configuration options.

## SemKernel Workflow org.dllearner.utilities.semkernel.SemKernelWorkflow

short name
  skw
version
  0.1
implements
  - OtherComponent

This component does not have configuration options.

## Stable Heuristic org.dllearner.algorithms.el.StableHeuristic

short name
  el_heuristic
version
  0.1
implements
  - Heuristic

This component does not have configuration options.

## TDT org.dllearner.algorithms.decisiontrees.tdt.TDTClassifier

short name
  tdt
version
  1.0
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm
description
  A Terminological Decision Tree

option name | description | type | default value | required?
--- | --- | --- | --- | ---
binaryClassification | if it is a binary classification problem | boolean | false | false
ccp | value for limiting the number of generated concepts | boolean | false | false
classToDescribe | concept for splitting undefined examples into positive and negative for binary classification problems | OWLClassExpression |  | false
heuristic | the heuristic instance to use | TreeInductionHeuristics | TreeInductionHeuristics | false
missingValueTreatmentForTDT | for overcoming the problem of missing values in tree algorithms.tree.models | boolean | false | false
operator | the refinement operator instance to use | RefinementOperator | DLTreesRefinementOperator | false
puritythreshold | Purity threshold for setting a leaf | double | 0.05 | false

## Weighted FMeasure org.dllearner.accuracymethods.AccMethodFMeasureWeighted

short name
  weighted.fmeasure
version
  0.0
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
balanced | balance the weights to relative set size | boolean | false | false
beta | beta factor (0 = do not use) | double | 0 | false
negWeight | weight on the negative examples | double | 1 | false
posWeight | weight on the positive examples | double | 1 | false

## Weighted Predictive Accuracy org.dllearner.accuracymethods.AccMethodPredAccWeighted

short name
  weighted.pred_acc
version
  0.0
implements
  - AccMethod

option name | description | type | default value | required?
--- | --- | --- | --- | ---
balanced | balance the weights to relative set size | boolean | false | false
negWeight | weight on the negative examples | double | 1 | false
posWeight | weight on the positive examples | double | 1 | false

## asymmetric object property axiom learner org.dllearner.algorithms.properties.AsymmetricObjectPropertyAxiomLearner

short name
  oplasymm
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for asymmetric object property axioms.

This component does not have configuration options.

## closed world reasoner org.dllearner.reasoning.ClosedWorldReasoner

short name
  cwr
version
  0.9
implements
  - ReasonerComponent

option name | description | type | default value | required?
--- | --- | --- | --- | ---
defaultNegation | Whether to use default negation, i.e. an instance not being in a class means that it is in the negation of the class. | boolean | true | false
forAllSemantics | This option controls how to interpret the all quantifier in forall r.C. The standard option is to return all those which do not have an r-filler not in C. The domain semantics is to use those which are in the domain of r and do not have an r-filler not in C. The forallExists semantics is to use those which have at least one r-filler and do not have an r-filler not in C. | ForallSemantics | standard | false
handlePunning | no description available | boolean | false | false
materializeExistentialRestrictions | no description available | boolean | false | false
precomputeClassHierarchy | if class hierarchy should be precomputed | boolean | true | false
precomputeDataPropertyHierarchy | no description available | boolean | true | false
precomputeObjectPropertyHierarchy | no description available | boolean | true | false
precomputeObjectPropertyRanges | if object property ranges should be precomputed | boolean | true | false
precomputePropertyDomains | if property domains should be precomputed | boolean | true | false
reasonerComponent | the underlying reasoner implementation | OWLAPIReasoner | OWL API Reasoner | false
sources | the underlying knowledge sources | Set |  | true
useInstanceChecks | whether to use single instance checks | boolean | false | false
useMaterializationCaching | no description available | boolean | true | false

## data property domain axiom learner org.dllearner.algorithms.properties.DataPropertyDomainAxiomLearner

short name
  dpldomain
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for data property domain axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false

## data property range learner org.dllearner.algorithms.properties.DataPropertyRangeAxiomLearner

short name
  dblrange
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for reflexive data property range axioms.

This component does not have configuration options.

## data subproperty axiom learner org.dllearner.algorithms.properties.SubDataPropertyOfAxiomLearner

short name
  dplsubprop
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm data subproperty axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false
beta | the beta value for the F-score calculation | double | 1.0 | false
strictMode | no description available | boolean | false | false

## disjoint classes learner org.dllearner.algorithms.DisjointClassesLearner

short name
  cldisjoint
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
  - ClassExpressionLearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
entityToDescribe | the OWL entity to learn about | OWLEntity |  | false
ks | the sparql endpoint knowledge source | SparqlEndpointKS |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds (abstract) | int | 10 | false
maxFetchedRows | The maximum number of rows fetched from the endpoint to approximate the result. | int |  | false
reasoner | The sparql reasoner instance to use | SPARQLReasoner | SPARQLReasoner | false
returnOnlyNewAxioms | omit axioms already existing in the knowledge base | boolean | false | false
suggestMostGeneralClasses | only keep most general classes in suggestions | boolean | true | false
useClassPopularity | include instance count / popularity when computing scores | boolean | true | false

## disjoint data properties axiom learner org.dllearner.algorithms.properties.DisjointDataPropertyAxiomLearner

short name
  dpldisjoint
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for disjoint data properties axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false
beta | the beta value for the F-score calculation | double | 1.0 | false
strictMode | no description available | boolean | false | false

## disjoint object properties axiom learner org.dllearner.algorithms.properties.DisjointObjectPropertyAxiomLearner

short name
  opldisjoint
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for disjoint object properties axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false
beta | the beta value for the F-score calculation | double | 1.0 | false
strictMode | no description available | boolean | false | false

## efficient SPARQL fragment extractor org.dllearner.kb.sparql.simple.SparqlSimpleExtractor

short name
  sparqls
version
  0.1
implements
  - KnowledgeSource

option name | description | type | default value | required?
--- | --- | --- | --- | ---
aboxfilter | Filter for the tbox, can use variable ?s, ?p amd ?o | String |  | false
defaultGraphURI | default graph URI | String |  | true
endpointURL | URL of the SPARQL endpoint | String |  | true
instances | List of the instances to use | List |  | true
ontologySchemaUrls | List of Ontology Schema URLs | List |  | true
recursionDepth | recursion depth | int |  | true
sparqlQuery | Sparql Query | String |  | false
tboxfilter | Filter for the tbox, can use variable ?example and ?class | String |  | false

## equivalent data properties axiom learner org.dllearner.algorithms.properties.EquivalentDataPropertyAxiomLearner

short name
  dplequiv
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for equivalent data properties axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false
beta | the beta value for the F-score calculation | double | 1.0 | false
strictMode | no description available | boolean | false | false

## equivalent object properties axiom learner org.dllearner.algorithms.properties.EquivalentObjectPropertyAxiomLearner

short name
  oplequiv
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for equivalent object properties axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false
beta | the beta value for the F-score calculation | double | 1.0 | false
strictMode | no description available | boolean | false | false

## functional data property axiom learner org.dllearner.algorithms.properties.FunctionalDataPropertyAxiomLearner

short name
  dplfunc
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for functional data property axioms.

This component does not have configuration options.

## functional object property axiom learner org.dllearner.algorithms.properties.FunctionalObjectPropertyAxiomLearner

short name
  oplfunc
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for functional object property axioms.

This component does not have configuration options.

## inverse functional object property axiom learner org.dllearner.algorithms.properties.InverseFunctionalObjectPropertyAxiomLearner

short name
  oplinvfunc
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for inverse functional object property axioms.

This component does not have configuration options.

## inverse object property axiom learner org.dllearner.algorithms.properties.InverseObjectPropertyAxiomLearner

short name
  oplinv
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for inverse object property axioms.

This component does not have configuration options.

## irreflexive object property axiom learner org.dllearner.algorithms.properties.IrreflexiveObjectPropertyAxiomLearner

short name
  oplirrefl
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for irreflexive object property axioms.

This component does not have configuration options.

## multiple criteria heuristic org.dllearner.algorithms.ocel.MultiHeuristic

short name
  multiheuristic
version
  0.7
implements
  - Heuristic

option name | description | type | default value | required?
--- | --- | --- | --- | ---
expansionPenaltyFactor | how much accuracy gain is worth an increase of horizontal expansion by one (typical value: 0.01) | double | 0.02 | false
gainBonusFactor | how accuracy gain should be weighted versus accuracy itself (typical value: 1.00) | double | 0.5 | false
negationPenalty | penalty value to deduce for using a negated class expression (complementOf) | int | 0 | false
negativeWeight | weighting factor on the number of true negatives (true positives are weigthed with 1) | double | 1.0 | false
nodeChildPenalty | penalty factor for the search tree node child count (use higher values for simple learning problems) | double | 0.0001 | false
startNodeBonus | the score value for the start node | double | 0.1 | false

## object property domain axiom learner org.dllearner.algorithms.properties.ObjectPropertyDomainAxiomLearner

short name
  opldomain
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for object property domain axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false

## object property range learner org.dllearner.algorithms.properties.ObjectPropertyRangeAxiomLearner

short name
  oplrange
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for object property range axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false

## object subproperty axiom learner org.dllearner.algorithms.properties.SubObjectPropertyOfAxiomLearner

short name
  oplsubprop
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm object subproperty axioms.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false
beta | the beta value for the F-score calculation | double | 1.0 | false
strictMode | no description available | boolean | false | false

## pattern-based learner org.dllearner.algorithms.pattern.PatternBasedAxiomLearningAlgorithm

short name
  patla
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  Pattern-based algorithm uses OWL axioms as pattern.

option name | description | type | default value | required?
--- | --- | --- | --- | ---
entityToDescribe | the OWL entity to learn about | OWLEntity |  | false
ks | the sparql endpoint knowledge source | SparqlEndpointKS |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds (abstract) | int | 10 | false
maxFetchedRows | The maximum number of rows fetched from the endpoint to approximate the result. | int |  | false
reasoner | The sparql reasoner instance to use | SPARQLReasoner | SPARQLReasoner | false
returnOnlyNewAxioms | omit axioms already existing in the knowledge base | boolean | false | false

## positive only learning problem org.dllearner.learningproblems.PosOnlyLP

short name
  posonlylp
version
  0.6
implements
  - LearningProblem

option name | description | type | default value | required?
--- | --- | --- | --- | ---
exampleLoaderHelper | load examples via class expression selector | ExampleLoader |  | false
positiveExamples | the positive examples | SortedSet |  | true

## query tree learner with noise (disjunctive) org.dllearner.algorithms.qtl.QTL2Disjunctive

short name
  qtl2dis
version
  0.8
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
beta | how important it is not to cover negatives | double | 1 | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
noisePercentage | the (approximated) percentage of noise within the examples | double | 0.0 | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false

## query tree learner with noise (disjunctive) - multi-threaded org.dllearner.algorithms.qtl.QTL2DisjunctiveMultiThreaded

short name
  qtl2dismt
version
  0.8
implements
  - LearningAlgorithm
  - ClassExpressionLearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
allowedConcepts | List of classes that are allowed | Set |  | false
allowedDataProperties | List of data properties to allow | Set |  | false
allowedObjectProperties | List of object properties to allow | Set |  | false
beta | how important it is not to cover negatives | double | 1 | false
ignoredConcepts | List of classes to ignore | Set |  | false
ignoredDataProperties | List of data properties to ignore | Set |  | false
ignoredObjectProperties | List of object properties to ignore | Set |  | false
learningProblem | The Learning Problem variable to use in this algorithm | AbstractClassExpressionLearningProblem |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds | long | 10 | false
noisePercentage | the (approximated) percentage of noise within the examples | double | 0.0 | false
reasoner | The reasoner variable to use for this learning problem | AbstractReasonerComponent |  | false
useMinimizer | Specifies whether returned expressions should be minimised by removing those parts, which are not needed. (Basically the minimiser tries to find the shortest expression which is equivalent to the learned expression). Turning this feature off may improve performance. | boolean | true | false

## reflexive object property axiom learner org.dllearner.algorithms.properties.ReflexiveObjectPropertyAxiomLearner

short name
  oplrefl
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for reflexive object property domain axioms.

This component does not have configuration options.

## rho refinement operator org.dllearner.refinementoperators.RhoDRDown

short name
  rho
version
  0.8
implements
  - RefinementOperator

option name | description | type | default value | required?
--- | --- | --- | --- | ---
applyAllFilter | no description available | boolean | true | false
applyExistsFilter | throwing out all refinements with duplicate ∃ r for any r | boolean | true | false
cardinalityLimit | limit for cardinality restrictions (this makes sense if we e.g. have compounds with too many atoms) | int | 5 | false
disjointChecks | skip combination of intersection between disjoint classes | boolean | true | false
dropDisjuncts | if enabled, generalise by removing parts of a disjunction | boolean | false | false
frequencyThreshold | minimum number an individual or literal has to be seen in the knowledge base before considering it for inclusion in concepts | int | 3 | false
instanceBasedDisjoints | no description available | boolean | true | false
lengthMetric | class expression length metric (should match learning algorithm usage) | OWLClassExpressionLengthMetric | default cel_metric | false
maxNrOfSplits | the number of generated split intervals for numeric types | int | 12 | false
reasoner | the reasoner to use | AbstractReasonerComponent |  | false
startClass | You can specify a start class for the algorithm | OWLClassExpression | owl:Thing | false
useAllConstructor | support of universal restrictions (owl:allValuesFrom), e.g. ∀ r.C | boolean | true | false
useBooleanDatatypes | support of boolean datatypes (xsd:boolean), e.g. ∃ r.{true} | boolean | true | false
useCardinalityRestrictions | support of qualified cardinality restrictions (owl:minCardinality, owl:maxCardinality, owl:exactCardinality), e.g. ≥ 3 r.C | boolean | true | false
useDataHasValueConstructor | support of has value constructor (owl:hasValue), e.g. ∃ r.{20} | boolean | false | false
useExistsConstructor | support of existential restrictions (owl:someValuesFrom), e.g. ∃ r.C | boolean | true | false
useHasSelf | support of local reflexivity of an object property expression (owl:hasSelf), e.g. ∃ loves.Self for a narcissistic | boolean | false | false
useHasValueConstructor | support of has value constructor (owl:hasValue), e.g. ∃ r.{a} | boolean | false | false
useInverse | support of inverse object properties (owl:inverseOf), e.g. r⁻.C | boolean | false | false
useNegation | support of negation (owl:complementOf), e.g. ¬ C | boolean | true | false
useNumericDatatypes | support of numeric datatypes (xsd:int, xsd:double, ...), e.g. ∃ r.{true} | boolean | true | false
useObjectValueNegation | whether to generate object complement while refining | boolean | false | false
useSomeOnly | universal restrictions on a property r are only used when there is already a cardinality and/or existential restriction on r | boolean | true | false
useStringDatatypes | support of string datatypes (xsd:string), e.g. ∃ r.{"SOME_STRING"} | boolean | false | false
useTimeDatatypes | no description available | boolean | true | false

## simple subclass learner org.dllearner.algorithms.SimpleSubclassLearner

short name
  clsub
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
  - ClassExpressionLearningAlgorithm

option name | description | type | default value | required?
--- | --- | --- | --- | ---
batchMode | compute everything in a single SPARQL query | boolean | false | false
entityToDescribe | the OWL entity to learn about | OWLEntity |  | false
ks | the sparql endpoint knowledge source | SparqlEndpointKS |  | false
maxExecutionTimeInSeconds | maximum execution of the algorithm in seconds (abstract) | int | 10 | false
maxFetchedRows | The maximum number of rows fetched from the endpoint to approximate the result. | int |  | false
reasoner | The sparql reasoner instance to use | SPARQLReasoner | SPARQLReasoner | false
returnOnlyNewAxioms | omit axioms already existing in the knowledge base | boolean | false | false
strictOWLMode | no description available | boolean | false | false

## symmetric object property axiom learner org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner

short name
  oplsymm
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for symmetric object property axioms.

This component does not have configuration options.

## transitive object property axiom learner org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner

short name
  opltrans
version
  0.1
implements
  - LearningAlgorithm
  - AxiomLearningAlgorithm
description
  A learning algorithm for transitive object property axioms.

This component does not have configuration options.