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[DL-Learner SVN] SF.net SVN: dl-learner:[3878] trunk
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From: <jen...@us...> - 2012-11-29 14:57:46
|
Revision: 3878
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3878&view=rev
Author: jenslehmann
Date: 2012-11-29 14:57:34 +0000 (Thu, 29 Nov 2012)
Log Message:
-----------
documentation update
Modified Paths:
--------------
trunk/components-core/src/main/java/org/dllearner/algorithms/el/ELLearningAlgorithmDisjunctive.java
trunk/components-core/src/main/java/org/dllearner/core/AnnComponentManager.java
trunk/components-core/src/main/java/org/dllearner/learningproblems/PosNegLPStrict.java
trunk/interfaces/doc/configOptions.html
Modified: trunk/components-core/src/main/java/org/dllearner/algorithms/el/ELLearningAlgorithmDisjunctive.java
===================================================================
--- trunk/components-core/src/main/java/org/dllearner/algorithms/el/ELLearningAlgorithmDisjunctive.java 2012-11-23 09:57:51 UTC (rev 3877)
+++ trunk/components-core/src/main/java/org/dllearner/algorithms/el/ELLearningAlgorithmDisjunctive.java 2012-11-29 14:57:34 UTC (rev 3878)
@@ -174,6 +174,7 @@
@Override
public void start() {
+// System.out.println("starting disjunctive ELTL algorithm");
stop = false;
isRunning = true;
reset();
@@ -196,7 +197,9 @@
// System.out.println("best: " + best);
// apply operator
+ System.out.print("applying operator ...");
List<ELDescriptionTree> refinements = operator.refine(best.getDescriptionTree());
+ System.out.println("done " + refinements.size() + " refinements");
// add all refinements to search tree, candidates, best descriptions
for(ELDescriptionTree refinement : refinements) {
addDescriptionTree(refinement, best);
Modified: trunk/components-core/src/main/java/org/dllearner/core/AnnComponentManager.java
===================================================================
--- trunk/components-core/src/main/java/org/dllearner/core/AnnComponentManager.java 2012-11-23 09:57:51 UTC (rev 3877)
+++ trunk/components-core/src/main/java/org/dllearner/core/AnnComponentManager.java 2012-11-29 14:57:34 UTC (rev 3878)
@@ -81,6 +81,7 @@
"org.dllearner.kb.sparql.SparqlKnowledgeSource",
"org.dllearner.kb.sparql.simple.SparqlSimpleExtractor",
"org.dllearner.learningproblems.PosNegLPStandard",
+ "org.dllearner.learningproblems.PosNegLPStrict",
"org.dllearner.learningproblems.FuzzyPosNegLPStandard",
"org.dllearner.learningproblems.PosOnlyLP",
"org.dllearner.learningproblems.ClassLearningProblem",
Modified: trunk/components-core/src/main/java/org/dllearner/learningproblems/PosNegLPStrict.java
===================================================================
--- trunk/components-core/src/main/java/org/dllearner/learningproblems/PosNegLPStrict.java 2012-11-23 09:57:51 UTC (rev 3877)
+++ trunk/components-core/src/main/java/org/dllearner/learningproblems/PosNegLPStrict.java 2012-11-29 14:57:34 UTC (rev 3878)
@@ -25,11 +25,12 @@
import java.util.TreeSet;
import org.dllearner.core.AbstractReasonerComponent;
+import org.dllearner.core.ComponentAnn;
import org.dllearner.core.ComponentInitException;
import org.dllearner.core.EvaluatedDescription;
+import org.dllearner.core.config.ConfigOption;
import org.dllearner.core.options.BooleanConfigOption;
import org.dllearner.core.options.ConfigEntry;
-import org.dllearner.core.options.ConfigOption;
import org.dllearner.core.options.DoubleConfigOption;
import org.dllearner.core.options.InvalidConfigOptionValueException;
import org.dllearner.core.owl.Description;
@@ -40,17 +41,27 @@
import org.dllearner.utilities.datastructures.SortedSetTuple;
/**
+ *
+ * A strict learning problem means that for negative examples, it is required that a negative example
+ * should be instance of (NOT C) for a hypothesis C.
+ *
* @author Jens Lehmann
*
*/
+@ComponentAnn(name = "PosNegLPStrict", shortName = "posNegStrict", version = 0.8)
public class PosNegLPStrict extends PosNegLP {
private Set<Individual> neutralExamples;
private boolean penaliseNeutralExamples = false;
private static final double defaultAccuracyPenalty = 1;
+
+ @ConfigOption(name = "accuracyPenalty", description = "penalty for incorrectness (example belongs neither to concept nor its negation)", defaultValue = "1", required = false)
private double accuracyPenalty = defaultAccuracyPenalty;
+
private static final double defaultErrorPenalty = 3;
+
+ @ConfigOption(name = "errorPenalty", description = "penalty for errors (example can be inferred to belong to the negated concept class)", defaultValue = "3", required = false)
private double errorPenalty = defaultErrorPenalty;
@@ -65,13 +76,14 @@
return "three valued definition learning problem";
}
+ /*
public static Collection<ConfigOption<?>> createConfigOptions() {
Collection<ConfigOption<?>> options = PosNegLP.createConfigOptions();
options.add(new BooleanConfigOption("penaliseNeutralExamples", "if set to true neutral examples are penalised"));
options.add(new DoubleConfigOption("accuracyPenalty", "penalty for pos/neg examples which are classified as neutral", defaultAccuracyPenalty));
options.add(new DoubleConfigOption("errorPenalty", "penalty for pos. examples classified as negative or vice versa", defaultErrorPenalty));
return options;
- }
+ } */
/*
* (non-Javadoc)
Modified: trunk/interfaces/doc/configOptions.html
===================================================================
--- trunk/interfaces/doc/configOptions.html 2012-11-23 09:57:51 UTC (rev 3877)
+++ trunk/interfaces/doc/configOptions.html 2012-11-29 14:57:34 UTC (rev 3878)
@@ -21,14 +21,19 @@
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.BruteForceLearner">Brute Force Learner</a></li></div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.celoe.CELOE">CELOE</a></li></div>
<div class="LearningProblem"><li><a href="#org.dllearner.learningproblems.ClassLearningProblem">ClassLearningProblem</a></li></div>
+<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.el.ELLearningAlgorithmDisjunctive">Disjunctive ELTL</a></li></div>
+<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.el.ELLearningAlgorithm">ELTL</a></li></div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.fuzzydll.FuzzyCELOE">Fuzzy CELOE</a></li></div>
<div class="ReasonerComponent"><li><a href="#org.dllearner.reasoning.fuzzydll.FuzzyOWLAPIReasoner">Fuzzy OWL API Reasoner</a></li></div>
<div class="LearningProblem"><li><a href="#org.dllearner.learningproblems.FuzzyPosNegLPStandard">FuzzyPosNegLPStandard</a></li></div>
<div class="KnowledgeSource"><li><a href="#org.dllearner.kb.KBFile">KB File</a></li></div>
+<div class="OtherComponent"><li><a href="#org.dllearner.algorithms.celoe.OEHeuristicRuntime">OEHeuristicRuntime</a></li></div>
<div class="ReasonerComponent"><li><a href="#org.dllearner.reasoning.OWLAPIReasoner">OWL API Reasoner</a></li></div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.ocel.OCEL">OWL Class Expression Learner</a></li></div>
<div class="KnowledgeSource"><li><a href="#org.dllearner.kb.OWLFile">OWL File</a></li></div>
+<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.celoe.PCELOE">PCELOE</a></li></div>
<div class="LearningProblem"><li><a href="#org.dllearner.learningproblems.PosNegLPStandard">PosNegLPStandard</a></li></div>
+<div class="LearningProblem"><li><a href="#org.dllearner.learningproblems.PosNegLPStrict">PosNegLPStrict</a></li></div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.RandomGuesser">Random Guesser</a></li></div>
<div class="KnowledgeSource"><li><a href="#org.dllearner.kb.SparqlEndpointKS">SPARQL endpoint</a></li></div>
<div class="KnowledgeSource"><li><a href="#org.dllearner.kb.sparql.SparqlKnowledgeSource">SPARQL endpoint fragment</a></li></div>
@@ -38,6 +43,7 @@
<div class="LearningAlgorithm AxiomLearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.DisjointClassesLearner">disjoint classes learner</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.DisjointDataPropertyAxiomLearner">disjoint dataproperty axiom learner</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.DisjointObjectPropertyAxiomLearner">disjoint objectproperty axiom learner</a></li></div>
+<div class="KnowledgeSource"><li><a href="#org.dllearner.kb.sparql.simple.SparqlSimpleExtractor">efficient SPARQL fragment extractor</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.EquivalentDataPropertyAxiomLearner">equivalent dataproperty axiom learner</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.EquivalentObjectPropertyAxiomLearner">equivalent objectproperty axiom learner</a></li></div>
<div class="ReasonerComponent"><li><a href="#org.dllearner.reasoning.FastInstanceChecker">fast instance checker</a></li></div>
@@ -49,34 +55,48 @@
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.ObjectPropertyDomainAxiomLearner">objectproperty domain axiom learner</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.ObjectPropertyRangeAxiomLearner">objectproperty range learner</a></li></div>
<div class="LearningProblem"><li><a href="#org.dllearner.learningproblems.PosOnlyLP">positive only learning problem</a></li></div>
-<div class="LearningAlgorithm"><li><a href="#org.dllearner.algorithm.qtl.QTL">query tree learner</a></li></div>
<div class="RefinementOperator"><li><a href="#org.dllearner.refinementoperators.RhoDRDown">rho refinement operator</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm ClassExpressionLearningAlgorithm"><li><a href="#org.dllearner.algorithms.SimpleSubclassLearner">simple subclass learner</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner">symmetric objectproperty axiom learner</a></li></div>
+<div class="RefinementOperator"><li><a href="#org.dllearner.refinementoperators.SynchronizedRhoDRDown">synchronized rho refinement operator</a></li></div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><li><a href="#org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner">transitive objectproperty axiom learner</a></li></div>
</ul>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.BruteForceLearner"><h2>Brute Force Learner</h2></a>
<p>short name: bruteForce<br />version: 0.8<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br /></p>This component does not have configuration options.</div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.celoe.CELOE"><h2>CELOE</h2></a>
<p>short name: celoe<br />version: 1.0<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br />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.<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>startClass</td><td>You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes.</td><td> Description</td><td>owl:Thing</td><td> false</td></tr>
+<tr><td>maxClassExpressionTests</td><td>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.)</td><td> int</td><td>0</td><td> false</td></tr>
+<tr><td>maxClassExpressionTestsAfterImprovement</td><td>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.)</td><td> int</td><td>0</td><td> false</td></tr>
+<tr><td>maxExecutionTimeInSecondsAfterImprovement</td><td>maximum execution of the algorithm in seconds</td><td> int</td><td>0</td><td> false</td></tr>
+<tr><td>replaceSearchTree</td><td>specifies whether to replace the search tree in the log file after each run or append the new search tree</td><td> boolean</td><td>false</td><td> false</td></tr>
<tr><td>useMinimizer</td><td>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.</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>filterDescriptionsFollowingFromKB</td><td>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.</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>stopOnFirstDefinition</td><td>algorithm will terminate immediately when a correct definition is found</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>reuseExistingDescription</td><td>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.</td><td> boolean</td><td>false</td><td> false</td></tr>
<tr><td>terminateOnNoiseReached</td><td>specifies whether to terminate when noise criterion is met</td><td> boolean</td><td>false</td><td> false</td></tr>
-<tr><td>reuseExistingDescription</td><td>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.</td><td> boolean</td><td>false</td><td> false</td></tr>
-<tr><td>filterDescriptionsFollowingFromKB</td><td>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.</td><td> boolean</td><td>false</td><td> false</td></tr>
-<tr><td>maxClassDescriptionTests</td><td>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.)</td><td> int</td><td>0</td><td> false</td></tr>
+<tr><td>noisePercentage</td><td>the (approximated) percentage of noise within the examples</td><td> double</td><td>0.0</td><td> false</td></tr>
<tr><td>maxDepth</td><td>maximum depth of description</td><td> double</td><td>7</td><td> false</td></tr>
-<tr><td>singleSuggestionMode</td><td>Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.</td><td> boolean</td><td>false</td><td> false</td></tr>
-<tr><td>noisePercentage</td><td>the (approximated) percentage of noise within the examples</td><td> double</td><td>0.0</td><td> false</td></tr>
+<tr><td>searchTreeFile</td><td>file to use for the search tree</td><td> String</td><td>log/searchTree.txt</td><td> false</td></tr>
<tr><td>maxExecutionTimeInSeconds</td><td>maximum execution of the algorithm in seconds</td><td> int</td><td>10</td><td> false</td></tr>
<tr><td>writeSearchTree</td><td>specifies whether to write a search tree</td><td> boolean</td><td>false</td><td> false</td></tr>
-<tr><td>startClass</td><td>You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes.</td><td> Description</td><td>owl:Thing</td><td> false</td></tr>
<tr><td>maxNrOfResults</td><td>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).</td><td> int</td><td>10</td><td> false</td></tr>
-<tr><td>searchTreeFile</td><td>file to use for the search tree</td><td> String</td><td>log/searchTree.txt</td><td> false</td></tr>
-<tr><td>replaceSearchTree</td><td>specifies whether to replace the search tree in the log file after each run or append the new search tree</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>singleSuggestionMode</td><td>Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.</td><td> boolean</td><td>false</td><td> false</td></tr>
</tbody></table>
</div>
<div class="LearningProblem"><a name="org.dllearner.learningproblems.ClassLearningProblem"><h2>ClassLearningProblem</h2></a>
<p>short name: clp<br />version: 0.6<br />implements: LearningProblem<br /></p>This component does not have configuration options.</div>
+<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.el.ELLearningAlgorithmDisjunctive"><h2>Disjunctive ELTL</h2></a>
+<p>short name: deltl<br />version: 0.5<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br />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.<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>tryFullCoverage</td><td>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.</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>treeSearchTimeSeconds</td><td>Specifies how long the algorithm should search for a partial solution (a tree).</td><td> double</td><td>1.0</td><td> false</td></tr>
+</tbody></table>
+</div>
+<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.el.ELLearningAlgorithm"><h2>ELTL</h2></a>
+<p>short name: eltl<br />version: 0.5<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br />description: ELTL is an algorithm based on the refinement operator in http://jens-lehmann.org/files/2009/el_ilp.pdf.<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>instanceBasedDisjoints</td><td>Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator.</td><td> boolean</td><td>true</td><td> false</td></tr>
+</tbody></table>
+</div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.fuzzydll.FuzzyCELOE"><h2>Fuzzy CELOE</h2></a>
<p>short name: fceloe<br />version: 0.2<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br />description: See Fuzzy DL-Learner paper published at ISDA 2011.<br /></p>This component does not have configuration options.</div>
<div class="ReasonerComponent"><a name="org.dllearner.reasoning.fuzzydll.FuzzyOWLAPIReasoner"><h2>Fuzzy OWL API Reasoner</h2></a>
@@ -85,33 +105,65 @@
<p>short name: fuzzyPosNeg<br />version: 0.2<br />implements: LearningProblem<br /></p>This component does not have configuration options.</div>
<div class="KnowledgeSource"><a name="org.dllearner.kb.KBFile"><h2>KB File</h2></a>
<p>short name: kbfile<br />version: 0.8<br />implements: KnowledgeSource<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>fileName</td><td>relative or absolute path to KB file</td><td> String</td><td></td><td> false</td></tr>
<tr><td>url</td><td>URL pointer to the KB file</td><td> String</td><td></td><td> false</td></tr>
-<tr><td>fileName</td><td>relative or absolute path to KB file</td><td> String</td><td></td><td> false</td></tr>
</tbody></table>
</div>
+<div class="OtherComponent"><a name="org.dllearner.algorithms.celoe.OEHeuristicRuntime"><h2>OEHeuristicRuntime</h2></a>
+<p>short name: celoe_heuristic<br />version: 0.5<br />implements: OtherComponent<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>startNodeBonus</td><td>no description available</td><td> double</td><td>0.1</td><td> false</td></tr>
+</tbody></table>
+</div>
<div class="ReasonerComponent"><a name="org.dllearner.reasoning.OWLAPIReasoner"><h2>OWL API Reasoner</h2></a>
<p>short name: oar<br />version: 0.8<br />implements: ReasonerComponent<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
<tr><td>owlLinkURL</td><td>The URL to the owl server</td><td> String</td><td></td><td> false</td></tr>
-<tr><td>reasonerType</td><td>The name of the OWL APIReasoner to use {"fact", "hermit", "owllink", "pellet"}</td><td> String</td><td>pellet</td><td> false</td></tr>
+<tr><td>reasonerType</td><td>The name of the OWL APIReasoner to use {"fact", "hermit", "owllink", "pellet", "elk", "cel"}</td><td> String</td><td>pellet</td><td> false</td></tr>
</tbody></table>
</div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.ocel.OCEL"><h2>OWL Class Expression Learner</h2></a>
<p>short name: ocel<br />version: 1.2<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br /></p>This component does not have configuration options.</div>
<div class="KnowledgeSource"><a name="org.dllearner.kb.OWLFile"><h2>OWL File</h2></a>
<p>short name: owlfile<br />version: 0.9<br />implements: KnowledgeSource<br /></p>This component does not have configuration options.</div>
+<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.celoe.PCELOE"><h2>PCELOE</h2></a>
+<p>short name: pceloe<br />version: 1.0<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br />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.<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>startClass</td><td>You can specify a start class for the algorithm. To do this, you have to use Manchester OWL syntax without using prefixes.</td><td> Description</td><td>owl:Thing</td><td> false</td></tr>
+<tr><td>maxClassExpressionTests</td><td>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.)</td><td> int</td><td>0</td><td> false</td></tr>
+<tr><td>maxClassExpressionTestsAfterImprovement</td><td>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.)</td><td> int</td><td>0</td><td> false</td></tr>
+<tr><td>maxExecutionTimeInSecondsAfterImprovement</td><td>maximum execution of the algorithm in seconds</td><td> int</td><td>0</td><td> false</td></tr>
+<tr><td>replaceSearchTree</td><td>specifies whether to replace the search tree in the log file after each run or append the new search tree</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>useMinimizer</td><td>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.</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>filterDescriptionsFollowingFromKB</td><td>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.</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>nrOfThreads</td><td>number of threads running in parallel</td><td> int</td><td>2</td><td> false</td></tr>
+<tr><td>reuseExistingDescription</td><td>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.</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>terminateOnNoiseReached</td><td>specifies whether to terminate when noise criterion is met</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>noisePercentage</td><td>the (approximated) percentage of noise within the examples</td><td> double</td><td>0.0</td><td> false</td></tr>
+<tr><td>maxDepth</td><td>maximum depth of description</td><td> double</td><td>7</td><td> false</td></tr>
+<tr><td>searchTreeFile</td><td>file to use for the search tree</td><td> String</td><td>log/searchTree.txt</td><td> false</td></tr>
+<tr><td>maxExecutionTimeInSeconds</td><td>maximum execution of the algorithm in seconds</td><td> int</td><td>10</td><td> false</td></tr>
+<tr><td>writeSearchTree</td><td>specifies whether to write a search tree</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>maxNrOfResults</td><td>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).</td><td> int</td><td>10</td><td> false</td></tr>
+<tr><td>singleSuggestionMode</td><td>Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.</td><td> boolean</td><td>false</td><td> false</td></tr>
+</tbody></table>
+</div>
<div class="LearningProblem"><a name="org.dllearner.learningproblems.PosNegLPStandard"><h2>PosNegLPStandard</h2></a>
<p>short name: posNegStandard<br />version: 0.8<br />implements: LearningProblem<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>approxDelta</td><td>The Approximate Delta</td><td> double</td><td>0.05</td><td> false</td></tr>
<tr><td>accuracyMethod</td><td>Specifies, which method/function to use for computing accuracy.</td><td> String</td><td>predacc</td><td> false</td></tr>
<tr><td>useApproximations</td><td>Use Approximations</td><td> boolean</td><td>false</td><td> false</td></tr>
-<tr><td>approxDelta</td><td>The Approximate Delta</td><td> double</td><td>0.05</td><td> false</td></tr>
</tbody></table>
</div>
+<div class="LearningProblem"><a name="org.dllearner.learningproblems.PosNegLPStrict"><h2>PosNegLPStrict</h2></a>
+<p>short name: posNegStrict<br />version: 0.8<br />implements: LearningProblem<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>accuracyPenalty</td><td>penalty for incorrectness (example belongs neither to concept nor its negation)</td><td> double</td><td>1</td><td> false</td></tr>
+<tr><td>errorPenalty</td><td>penalty for errors (example can be inferred to belong to the negated concept class)</td><td> double</td><td>3</td><td> false</td></tr>
+</tbody></table>
+</div>
<div class="LearningAlgorithm ClassExpressionLearningAlgorithm"><a name="org.dllearner.algorithms.RandomGuesser"><h2>Random Guesser</h2></a>
<p>short name: randomGuesser<br />version: 0.8<br />implements: LearningAlgorithm, ClassExpressionLearningAlgorithm<br /></p>This component does not have configuration options.</div>
<div class="KnowledgeSource"><a name="org.dllearner.kb.SparqlEndpointKS"><h2>SPARQL endpoint</h2></a>
<p>short name: sparql<br />version: 0.2<br />implements: KnowledgeSource<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>namedGraphs</td><td>no description available</td><td> List</td><td>[]</td><td> false</td></tr>
<tr><td>defaultGraphs</td><td>no description available</td><td> List</td><td>[]</td><td> false</td></tr>
-<tr><td>namedGraphs</td><td>no description available</td><td> List</td><td>[]</td><td> false</td></tr>
<tr><td>url</td><td>no description available</td><td> URL</td><td></td><td> true</td></tr>
</tbody></table>
</div>
@@ -147,6 +199,18 @@
<tr><td>propertyToDescribe</td><td></td><td> ObjectProperty</td><td></td><td> false</td></tr>
</tbody></table>
</div>
+<div class="KnowledgeSource"><a name="org.dllearner.kb.sparql.simple.SparqlSimpleExtractor"><h2>efficient SPARQL fragment extractor</h2></a>
+<p>short name: sparqls<br />version: 0.1<br />implements: KnowledgeSource<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>tboxfilter</td><td>Filter for the tbox, can use variable ?example and ?class</td><td> String</td><td></td><td> false</td></tr>
+<tr><td>endpointURL</td><td>URL of the SPARQL endpoint</td><td> String</td><td></td><td> true</td></tr>
+<tr><td>ontologySchemaUrls</td><td>List of Ontology Schema URLs</td><td> List</td><td></td><td> true</td></tr>
+<tr><td>aboxfilter</td><td>Filter for the tbox, can use variable ?s, ?p amd ?o</td><td> String</td><td></td><td> false</td></tr>
+<tr><td>instances</td><td>List of the instances to use</td><td> List</td><td></td><td> true</td></tr>
+<tr><td>sparqlQuery</td><td>Sparql Query</td><td> String</td><td></td><td> false</td></tr>
+<tr><td>defaultGraphURI</td><td>default graph URI</td><td> String</td><td></td><td> true</td></tr>
+<tr><td>recursionDepth</td><td>recursion depth</td><td> int</td><td></td><td> true</td></tr>
+</tbody></table>
+</div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><a name="org.dllearner.algorithms.properties.EquivalentDataPropertyAxiomLearner"><h2>equivalent dataproperty axiom learner</h2></a>
<p>short name: dplequiv<br />version: 0.1<br />implements: LearningAlgorithm, AxiomLearningAlgorithm<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
<tr><td>propertyToDescribe</td><td></td><td> DatatypeProperty</td><td></td><td> false</td></tr>
@@ -180,12 +244,12 @@
</div>
<div class="OtherComponent"><a name="org.dllearner.algorithms.ocel.MultiHeuristic"><h2>multiple criteria heuristic</h2></a>
<p>short name: multiheuristic<br />version: 0.7<br />implements: OtherComponent<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>expansionPenaltyFactor</td><td>no description available</td><td> double</td><td>0.02</td><td> false</td></tr>
+<tr><td>gainBonusFactor</td><td>no description available</td><td> double</td><td>0.5</td><td> false</td></tr>
+<tr><td>startNodeBonus</td><td>no description available</td><td> double</td><td>0.1</td><td> false</td></tr>
<tr><td>negationPenalty</td><td>no description available</td><td> int</td><td>0</td><td> false</td></tr>
-<tr><td>expansionPenaltyFactor</td><td>no description available</td><td> double</td><td>0.02</td><td> false</td></tr>
<tr><td>negativeWeight</td><td>no description available</td><td> double</td><td>1.0</td><td> false</td></tr>
-<tr><td>gainBonusFactor</td><td>no description available</td><td> double</td><td>0.5</td><td> false</td></tr>
<tr><td>nodeChildPenalty</td><td>no description available</td><td> double</td><td>0.0001</td><td> false</td></tr>
-<tr><td>startNodeBonus</td><td>no description available</td><td> double</td><td>0.1</td><td> false</td></tr>
</tbody></table>
</div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><a name="org.dllearner.algorithms.properties.SubObjectPropertyOfAxiomLearner"><h2>object subPropertyOf axiom learner</h2></a>
@@ -205,22 +269,20 @@
</div>
<div class="LearningProblem"><a name="org.dllearner.learningproblems.PosOnlyLP"><h2>positive only learning problem</h2></a>
<p>short name: posonlylp<br />version: 0.6<br />implements: LearningProblem<br /></p>This component does not have configuration options.</div>
-<div class="LearningAlgorithm"><a name="org.dllearner.algorithm.qtl.QTL"><h2>query tree learner</h2></a>
-<p>short name: qtl<br />version: 0.8<br />implements: LearningAlgorithm<br /></p>This component does not have configuration options.</div>
<div class="RefinementOperator"><a name="org.dllearner.refinementoperators.RhoDRDown"><h2>rho refinement operator</h2></a>
<p>short name: rho<br />version: 0.8<br />implements: RefinementOperator<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>useAllConstructor</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useHasValueConstructor</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>instanceBasedDisjoints</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>dropDisjuncts</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
<tr><td>applyAllFilter</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useDoubleDatatypes</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
<tr><td>useNegation</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
-<tr><td>applyExistsFilter</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
-<tr><td>useCardinalityRestrictions</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
<tr><td>useStringDatatypes</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
-<tr><td>useHasValueConstructor</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
<tr><td>useBooleanDatatypes</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
-<tr><td>useDoubleDatatypes</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
-<tr><td>instanceBasedDisjoints</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
-<tr><td>useAllConstructor</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
-<tr><td>dropDisjuncts</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
<tr><td>useExistsConstructor</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useCardinalityRestrictions</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>applyExistsFilter</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
<tr><td>disjointChecks</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
</tbody></table>
</div>
@@ -234,6 +296,23 @@
<tr><td>propertyToDescribe</td><td></td><td> ObjectProperty</td><td></td><td> false</td></tr>
</tbody></table>
</div>
+<div class="RefinementOperator"><a name="org.dllearner.refinementoperators.SynchronizedRhoDRDown"><h2>synchronized rho refinement operator</h2></a>
+<p>short name: syncrho<br />version: 0.8<br />implements: RefinementOperator<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
+<tr><td>useAllConstructor</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useHasValueConstructor</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>instanceBasedDisjoints</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>dropDisjuncts</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>applyAllFilter</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useDoubleDatatypes</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useNegation</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useStringDatatypes</td><td>no description available</td><td> boolean</td><td>false</td><td> false</td></tr>
+<tr><td>useBooleanDatatypes</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useExistsConstructor</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>useCardinalityRestrictions</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>applyExistsFilter</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+<tr><td>disjointChecks</td><td>no description available</td><td> boolean</td><td>true</td><td> false</td></tr>
+</tbody></table>
+</div>
<div class="LearningAlgorithm AxiomLearningAlgorithm"><a name="org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner"><h2>transitive objectproperty axiom learner</h2></a>
<p>short name: opltrans<br />version: 0.1<br />implements: LearningAlgorithm, AxiomLearningAlgorithm<br /></p><table id="hor-minimalist-a"><thead><tr><th>option name</th><th>description</th><th>type</th><th>default value</th><th>required?</th></tr></thead><tbody>
<tr><td>propertyToDescribe</td><td></td><td> ObjectProperty</td><td></td><td> false</td></tr>
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