Click on the following items to filter the listing below by implemented interfaces (requires Javascript):
show allClick on a component to get an overview on its configuration options.
option name | description | type | default value | required? |
---|---|---|---|---|
beta | beta factor (0 = do not use) | double | 0 | false |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
option name | description | type | default value | required? |
---|---|---|---|---|
beta | beta factor (0 = do not use) | double | 0 | false |
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 |
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 |
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 |
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 |
option name | description | type | default value | required? |
---|---|---|---|---|
beta | beta factor (0 = do not use) | double | 0 | false |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
option name | description | type | default value | required? |
---|---|---|---|---|
uncertainExamples | the uncertain examples | Set | true |
option name | description | type | default value | required? |
---|---|---|---|---|
beta | beta factor (0 = do not use) | double | 0 | false |
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 |
option name | description | type | default value | required? |
---|---|---|---|---|
reasoner | The reasoner component variable to use for this Learning Problem | AbstractReasonerComponent | false |
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 |
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 |
requestLogFile | Log file for reasoner request logging | String | false | |
requestLogging | Log reasoner requests | boolean | false | 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 |
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 |
requestLogFile | Log file for reasoner request logging | String | false | |
requestLogging | Log reasoner requests | boolean | false | 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 |
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 |
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 |
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 |
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 |
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 |
option name | description | type | default value | required? |
---|---|---|---|---|
batchMode | compute everything in a single SPARQL query | boolean | false | false |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
option name | description | type | default value | required? |
---|---|---|---|---|
batchMode | compute everything in a single SPARQL query | boolean | false | false |
option name | description | type | default value | required? |
---|---|---|---|---|
batchMode | compute everything in a single SPARQL query | boolean | false | false |
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 |
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 |
option name | description | type | default value | required? |
---|---|---|---|---|
exampleLoaderHelper | load examples via class expression selector | ExampleLoader | false | |
positiveExamples | the positive examples | SortedSet | true |
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 |
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 |
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 |
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 |