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[DL-Learner SVN] SF.net SVN: dl-learner:[3123] trunk/scripts/src/main/java/org/dllearner/ scripts/evaluation/EnrichmentEvaluation.java
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From: <lor...@us...> - 2011-08-25 12:18:03
|
Revision: 3123
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3123&view=rev
Author: lorenz_b
Date: 2011-08-25 12:17:57 +0000 (Thu, 25 Aug 2011)
Log Message:
-----------
Continued eval script.
Modified Paths:
--------------
trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java
Modified: trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java
===================================================================
--- trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-25 12:16:46 UTC (rev 3122)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-25 12:17:57 UTC (rev 3123)
@@ -32,14 +32,19 @@
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
+import java.sql.ResultSet;
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Statement;
import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
import java.util.HashMap;
+import java.util.HashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
+import java.util.Map.Entry;
import java.util.Set;
import java.util.prefs.Preferences;
@@ -52,10 +57,19 @@
import org.apache.log4j.SimpleLayout;
import org.dllearner.algorithms.DisjointClassesLearner;
import org.dllearner.algorithms.SimpleSubclassLearner;
+import org.dllearner.algorithms.celoe.CELOE;
import org.dllearner.algorithms.properties.DataPropertyDomainAxiomLearner;
import org.dllearner.algorithms.properties.DataPropertyRangeAxiomLearner;
+import org.dllearner.algorithms.properties.DisjointDataPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.DisjointObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.EquivalentDataPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.EquivalentObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.FunctionalDataPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.FunctionalObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.InverseFunctionalObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.IrreflexiveObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.ObjectPropertyDomainAxiomLearner;
+import org.dllearner.algorithms.properties.ObjectPropertyRangeAxiomLearner;
import org.dllearner.algorithms.properties.ReflexiveObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.SubDataPropertyOfAxiomLearner;
import org.dllearner.algorithms.properties.SubObjectPropertyOfAxiomLearner;
@@ -70,7 +84,9 @@
import org.dllearner.core.EvaluatedDescription;
import org.dllearner.core.LearningAlgorithm;
import org.dllearner.core.config.ConfigHelper;
+import org.dllearner.core.owl.Axiom;
import org.dllearner.core.owl.DatatypeProperty;
+import org.dllearner.core.owl.DisjointClassesAxiom;
import org.dllearner.core.owl.NamedClass;
import org.dllearner.core.owl.ObjectProperty;
import org.dllearner.kb.SparqlEndpointKS;
@@ -79,16 +95,28 @@
import org.dllearner.learningproblems.AxiomScore;
import org.dllearner.utilities.CommonPrefixMap;
import org.dllearner.utilities.Files;
+import org.dllearner.utilities.owl.DLLearnerAxiomConvertVisitor;
import org.dllearner.utilities.owl.OWLAPIConverter;
import org.ini4j.IniPreferences;
import org.ini4j.InvalidFileFormatException;
import org.semanticweb.owlapi.apibinding.OWLManager;
+import org.semanticweb.owlapi.model.AxiomType;
+import org.semanticweb.owlapi.model.IRI;
import org.semanticweb.owlapi.model.OWLAxiom;
+import org.semanticweb.owlapi.model.OWLClassAxiom;
+import org.semanticweb.owlapi.model.OWLDataFactory;
+import org.semanticweb.owlapi.model.OWLDataPropertyAxiom;
+import org.semanticweb.owlapi.model.OWLDisjointClassesAxiom;
+import org.semanticweb.owlapi.model.OWLEntity;
+import org.semanticweb.owlapi.model.OWLObjectPropertyAxiom;
import org.semanticweb.owlapi.model.OWLOntology;
import org.semanticweb.owlapi.model.OWLOntologyCreationException;
+import org.semanticweb.owlapi.model.OWLSubClassOfAxiom;
import org.semanticweb.owlapi.reasoner.InferenceType;
import org.semanticweb.owlapi.reasoner.OWLReasoner;
+import uk.ac.manchester.cs.owl.owlapi.OWLDataFactoryImpl;
+
import com.clarkparsia.pellet.owlapiv3.PelletReasonerFactory;
/**
@@ -100,6 +128,13 @@
public class EnrichmentEvaluation {
private static Logger logger = Logger.getLogger(EnrichmentEvaluation.class);
+
+ // max. number of attempts per algorithm and entity, because to many queries
+ // in a short time could cause blocking by the endpoint
+ private final int maxAttempts = 3;
+
+ //delay between 2 attempts
+ private final int delayInMilliseconds = 10000;
// max. execution time for each learner for each entity
private int maxExecutionTimeInSeconds = 10;
@@ -107,6 +142,11 @@
// number of axioms which will be learned/considered (only applies to
// some learners)
private int nrOfAxiomsToLearn = 10;
+
+ // only axioms with a score above this threshold will be considered
+ private double threshold = 0.7;
+
+ private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();//getEndpointDBpediaLiveAKSW();
// can be used to only evaluate a part of DBpedia
private int maxObjectProperties = 3;
@@ -124,6 +164,7 @@
private OWLOntology dbPediaOntology;
private OWLReasoner reasoner;
+ private OWLDataFactory factory = new OWLDataFactoryImpl();
public EnrichmentEvaluation() {
@@ -132,12 +173,12 @@
prefixes.put("dbo","http://dbpedia.org/ontology/");
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
-// objectPropertyAlgorithms.add(DisjointObjectPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(EquivalentObjectPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(FunctionalObjectPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(InverseFunctionalObjectPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(ObjectPropertyDomainAxiomLearner.class);
-// objectPropertyAlgorithms.add(ObjectPropertyRangeAxiomLearner.class);
+ objectPropertyAlgorithms.add(DisjointObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(EquivalentObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(FunctionalObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(InverseFunctionalObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(ObjectPropertyDomainAxiomLearner.class);
+ objectPropertyAlgorithms.add(ObjectPropertyRangeAxiomLearner.class);
objectPropertyAlgorithms.add(SubObjectPropertyOfAxiomLearner.class);
objectPropertyAlgorithms.add(SymmetricObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(TransitiveObjectPropertyAxiomLearner.class);
@@ -145,8 +186,8 @@
objectPropertyAlgorithms.add(ReflexiveObjectPropertyAxiomLearner.class);
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
-// dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
-// dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
+ dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
+ dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
dataPropertyAlgorithms.add(DataPropertyRangeAxiomLearner.class);
@@ -222,22 +263,25 @@
public void start() throws IllegalArgumentException, SecurityException, InstantiationException,
IllegalAccessException, InvocationTargetException, NoSuchMethodException,
- ComponentInitException {
+ ComponentInitException, InterruptedException {
dropAndCreateTable();
long overallStartTime = System.currentTimeMillis();
// ComponentManager cm = ComponentManager.getInstance();
// create DBpedia Live knowledge source
- SparqlEndpoint se = SparqlEndpoint.getEndpointDBpediaLiveAKSW();
- SparqlEndpointKS ks = new SparqlEndpointKS(se);
+ SparqlEndpointKS ks = new SparqlEndpointKS(endpoint);
ks.init();
evaluateObjectProperties(ks);
+//
+// Thread.sleep(10000);
evaluateDataProperties(ks);
+// Thread.sleep(10000);
+//
evaluateClasses(ks);
System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
@@ -264,20 +308,36 @@
// learner.setPropertyToDescribe(property);
// learner.setMaxExecutionTimeInSeconds(10);
algName = AnnComponentManager.getName(learner);
- System.out.println("Applying " + algName + " on " + property + " ... ");
- long startTime = System.currentTimeMillis();
- boolean timeout = false;
- try {
- learner.start();
- } catch (Exception e) {
- if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;throw e;
+
+ int attempt = 0;
+ long startTime = 0;
+ boolean timeout = true;
+ while(timeout && attempt++ < maxAttempts){
+ timeout = false;
+ if(attempt > 1){
+ try {
+ Thread.sleep(delayInMilliseconds);
+ } catch (InterruptedException e) {
+ e.printStackTrace();
+ }
}
+ System.out.println("Applying " + algName + " on " + property + " ... (Attempt " + attempt + ")");
+ startTime = System.currentTimeMillis();
+ try {
+ learner.start();
+ } catch (Exception e) {
+ if(e.getCause() instanceof SocketTimeoutException){
+ timeout = true;
+ } else {
+ e.printStackTrace();
+ }
+ }
}
+
long runTime = System.currentTimeMillis() - startTime;
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
- if(timeout){
+ if(timeout && learnedAxioms.isEmpty()){
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
} else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
@@ -324,20 +384,36 @@
// learner.setPropertyToDescribe(property);
// learner.setMaxExecutionTimeInSeconds(10);
algName = AnnComponentManager.getName(learner);
- System.out.println("Applying " + algName + " on " + property + " ... ");
- long startTime = System.currentTimeMillis();
- boolean timeout = false;
- try {
- learner.start();
- } catch (Exception e) {
- if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
+ int attempt = 0;
+ long startTime = 0;
+ boolean timeout = true;
+ while(timeout && attempt++ < maxAttempts){
+ timeout = false;
+ if(attempt > 1){
+ try {
+ Thread.sleep(delayInMilliseconds);
+ } catch (InterruptedException e) {
+ e.printStackTrace();
+ }
}
+ System.out.println("Applying " + algName + " on " + property + " ... (Attempt " + attempt + ")");
+ startTime = System.currentTimeMillis();
+ try {
+ learner.start();
+ } catch (Exception e) {
+ if(e.getCause() instanceof SocketTimeoutException){
+ timeout = true;
+ } else {
+ e.printStackTrace();
+ }
+ }
}
+
long runTime = System.currentTimeMillis() - startTime;
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
- if(timeout){
+ if(timeout && learnedAxioms.isEmpty()){
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
} else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
@@ -381,16 +457,32 @@
// learner.setPropertyToDescribe(property);
// learner.setMaxExecutionTimeInSeconds(10);
String algName = AnnComponentManager.getName(learner);
- System.out.println("Applying " + algName + " on " + cls + " ... ");
- long startTime = System.currentTimeMillis();
- boolean timeout = false;
- try {
- learner.start();
- } catch (Exception e) {
- if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
+ int attempt = 0;
+ long startTime = 0;
+ boolean timeout = true;
+ while(timeout && attempt++ < maxAttempts){
+ timeout = false;
+ if(attempt > 1){
+ try {
+ Thread.sleep(delayInMilliseconds);
+ } catch (InterruptedException e) {
+ e.printStackTrace();
+ }
}
+ System.out.println("Applying " + algName + " on " + cls + " ... (Attempt " + attempt + ")");
+ startTime = System.currentTimeMillis();
+ try {
+ learner.start();
+ } catch (Exception e) {
+ if(e.getCause() instanceof SocketTimeoutException){
+ timeout = true;
+ } else {
+ e.printStackTrace();
+ }
+ }
}
+
long runTime = System.currentTimeMillis() - startTime;
List<EvaluatedAxiom> learnedAxioms = null;
if(learner instanceof AxiomLearningAlgorithm){
@@ -436,13 +528,21 @@
}
public void printResultsLaTeX() throws Exception{
- double threshold = 0.9;
-
List<Class<? extends LearningAlgorithm>> algorithms = new ArrayList<Class<? extends LearningAlgorithm>>();
algorithms.addAll(classAlgorithms);
algorithms.addAll(objectPropertyAlgorithms);
algorithms.addAll(dataPropertyAlgorithms);
+ //create view which contains only entries without TIMEOUT and NULL
+ PreparedStatement ps = conn.prepareStatement("CREATE OR REPLACE VIEW evaluation_cleaned AS (SELECT * FROM evaluation WHERE axiom != ? AND axiom != ?)");
+ ps.setString(1, "NULL");
+ ps.setString(2, "TIMEOUT");
+ ps.execute();
+
+ StringBuilder table1 = new StringBuilder();
+ table1.append("\\begin{tabulary}{\\textwidth}{LCCCCC}\\toprule\n");
+ table1.append(" algorithm & Avg. \\#suggestions & Avg. runtime in ms & timeout in \\% & Avg. score & Avg. maximum score\\\\\\midrule\n");
+
for(Class<? extends LearningAlgorithm> algo : algorithms){
String algoName = algo.getAnnotation(ComponentAnn.class).name();
@@ -470,12 +570,12 @@
int numberOfEntitiesWithTimeout = rs.getInt(1);
//compute average number of suggestions above threshold
- PreparedStatement ps = conn.prepareStatement("SELECT AVG(cnt) FROM (SELECT entity, COUNT(axiom) AS cnt FROM (SELECT * FROM evaluation WHERE algorithm=? AND score >=?) AS A GROUP BY entity) AS B");
+ ps = conn.prepareStatement("SELECT AVG(cnt) FROM (SELECT entity, COUNT(axiom) AS cnt FROM (SELECT * FROM evaluation WHERE algorithm=? AND score >=?) AS A GROUP BY entity) AS B");
ps.setString(1, algoName);
ps.setDouble(2, threshold);
rs = ps.executeQuery();
rs.next();
- double avgSuggestionsAboveThreshold = rs.getDouble(1);
+ double avgSuggestionsAboveThreshold = round(rs.getDouble(1));
//compute average runtime
ps = conn.prepareStatement("SELECT AVG(runtime) FROM (SELECT MAX(runtime_ms) AS runtime FROM evaluation WHERE algorithm=?) AS A");
@@ -485,38 +585,154 @@
double avgRuntimeInMilliseconds = rs.getDouble(1);
//compute ratio for complete timeouts
- double timeoutRatio = (double)numberOfEntitiesWithTimeout / overallNumberOfEntities;
+ double timeoutRatio = round((double)numberOfEntitiesWithTimeout / overallNumberOfEntities);
//compute avg. score
- ps = conn.prepareStatement("SELECT AVG(avg) FROM (SELECT AVG(score) AS avg FROM evaluation WHERE algorithm=? AND score >= ? GROUP BY entity) AS A");
+ ps = conn.prepareStatement("SELECT AVG(avg) FROM (SELECT AVG(score) AS avg FROM evaluation_cleaned WHERE algorithm=? AND score >= ? GROUP BY entity) AS A");
ps.setString(1, algoName);
ps.setDouble(2, threshold);
rs = ps.executeQuery();
rs.next();
- double avgScore= rs.getDouble(1);
+ double avgScore = round(rs.getDouble(1));
//compute avg. max. score
- ps = conn.prepareStatement("SELECT AVG(max) FROM (SELECT MAX(score) AS max FROM evaluation WHERE algorithm=? AND score>=? GROUP BY entity) AS A");
+ ps = conn.prepareStatement("SELECT AVG(max) FROM (SELECT MAX(score) AS max FROM evaluation_cleaned WHERE algorithm=? AND score>=? GROUP BY entity) AS A");
ps.setString(1, algoName);
ps.setDouble(2, threshold);
rs = ps.executeQuery();
rs.next();
- double avgMaxScore = rs.getDouble(1);
+ double avgMaxScore = round(rs.getDouble(1));
System.out.println("###############");
System.out.println(algoName);
System.out.println("Overall entities: " + overallNumberOfEntities);
System.out.println("Overall entities with emtpy result: " + numberOfEntitiesWithEmptyResult);
System.out.println("Overall entities with emtpy timeout: " + numberOfEntitiesWithTimeout);
- System.out.println("Avg. #Suggestions above t: " + avgSuggestionsAboveThreshold);
+ System.out.println("Avg. \\#Suggestions above t: " + avgSuggestionsAboveThreshold);
System.out.println("Avg. runtime in ms: " + avgRuntimeInMilliseconds);
System.out.println("Timeout ratio: " + timeoutRatio);
System.out.println("Avg. score: " + avgScore);
System.out.println("Avg. max. score: " + avgMaxScore);
+ table1.
+ append(algoName.replace("learner", "").trim()).append(" & ").
+ append(avgSuggestionsAboveThreshold).append(" & ").
+ append(avgRuntimeInMilliseconds).append(" & ").
+ append(timeoutRatio).append(" & ").
+ append(avgScore).append(" & ").
+ append(avgMaxScore).
+ append("\\\\\n");
+
}
+ table1.append("\\bottomrule\n\\end{tabulary}");
+ System.out.println(table1.toString());
+
+
+ //get all axiomtypes and corresponding algorithm
+ Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> axiomType2Algorithm = new HashMap<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>>();
+ axiomType2Algorithm.put(AxiomType.SUBCLASS_OF, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SimpleSubclassLearner.class, CELOE.class}));
+ axiomType2Algorithm.put(AxiomType.EQUIVALENT_CLASSES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{CELOE.class}));
+ axiomType2Algorithm.put(AxiomType.DISJOINT_CLASSES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DisjointClassesLearner.class}));
+
+ axiomType2Algorithm.put(AxiomType.SUB_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SubObjectPropertyOfAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.EQUIVALENT_OBJECT_PROPERTIES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{EquivalentObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.OBJECT_PROPERTY_DOMAIN, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ObjectPropertyDomainAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.OBJECT_PROPERTY_RANGE, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ObjectPropertyRangeAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.TRANSITIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{TransitiveObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalDataPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.INVERSE_FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{InverseFunctionalObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.SYMMETRIC_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SymmetricObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.REFLEXIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{ReflexiveObjectPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.IRREFLEXIVE_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{IrreflexiveObjectPropertyAxiomLearner.class}));
+
+ axiomType2Algorithm.put(AxiomType.SUB_DATA_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SubDataPropertyOfAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.EQUIVALENT_DATA_PROPERTIES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{EquivalentDataPropertyAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.DATA_PROPERTY_DOMAIN, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DataPropertyDomainAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.DATA_PROPERTY_RANGE, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{DataPropertyRangeAxiomLearner.class}));
+ axiomType2Algorithm.put(AxiomType.FUNCTIONAL_DATA_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalDataPropertyAxiomLearner.class}));
+
+ // get all entities in database because we compute recall only for axioms of entities which we have tested
+ // we use only entities for which triples in the endpoint are contained
+ java.sql.ResultSet rs = conn.prepareStatement("SELECT DISTINCT entity FROM evaluation WHERE axiom != 'NULL'").executeQuery();
+ Set<OWLEntity> entities = new HashSet<OWLEntity>();
+ IRI iri;
+ while(rs.next()){
+ iri = IRI.create("http://dbpedia.org/ontology/" + rs.getString(1).substring(4));
+ if(dbPediaOntology.containsClassInSignature(iri)){
+ entities.add(factory.getOWLClass(iri));
+ } else if(dbPediaOntology.containsObjectPropertyInSignature(iri)){
+ entities.add(factory.getOWLObjectProperty(iri));
+ } else if(dbPediaOntology.containsDataPropertyInSignature(iri)){
+ entities.add(factory.getOWLDataProperty(iri));
+ }
+ }
+
+ //compute for each axiom type the recall
+ for(Entry<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> entry : axiomType2Algorithm.entrySet()){
+ AxiomType<? extends OWLAxiom> type = entry.getKey();
+ algorithms = entry.getValue();
+
+ int found = 0;
+ int all = 0;
+ for(OWLAxiom axiom : dbPediaOntology.getAxioms(type)){
+ if(isRelevantAxiom(axiom, entities)){
+ all++;
+ if(existsInDatabase(axiom)){
+ found++;
+ } else {
+ System.out.println(axiom);
+ }
+ }
+ }
+ System.out.println(type.getName() + ": " + found + "/" + all);
+ }
}
+
+ private boolean isRelevantAxiom(OWLAxiom axiom, Set<OWLEntity> entities){
+ if(axiom instanceof OWLObjectPropertyAxiom){
+ return containsOneOf(axiom.getObjectPropertiesInSignature(), entities);
+ } else if(axiom instanceof OWLDataPropertyAxiom){
+ return containsOneOf(axiom.getDataPropertiesInSignature(), entities);
+ } else if(axiom instanceof OWLSubClassOfAxiom){
+ return entities.contains(((OWLSubClassOfAxiom) axiom).getSubClass());
+ } else if(axiom instanceof OWLDisjointClassesAxiom){
+ return containsOneOf(axiom.getClassesInSignature(), entities);
+ }
+ return false;
+ }
+
+ private <T> boolean containsOneOf(Collection<? extends T> c1, Collection<? extends T> c2){
+ for(T element : c2){
+ if(c1.contains(element)){
+ return true;
+ }
+ }
+ return false;
+ }
+
+ private boolean existsInDatabase(OWLAxiom ax){
+ //if axiom contains owl:Thing it is trivially contained, so we can return TRUE here
+ if(ax.getClassesInSignature().contains(factory.getOWLThing())){
+ return true;
+ }
+ try {
+ Axiom axiom = DLLearnerAxiomConvertVisitor.getDLLearnerAxiom(ax);
+ PreparedStatement ps = conn.prepareStatement("SELECT axiom FROM evaluation WHERE axiom = ?");
+ ps.setString(1, axiom.toManchesterSyntaxString(baseURI, prefixes));
+ ResultSet rs = ps.executeQuery();
+ boolean exists = rs.next();
+ ps.close();
+ return exists;
+ } catch (SQLException e) {
+ e.printStackTrace();
+ }
+ return false;
+ }
+
+ private double round(double value){
+ return Math.round( value * 100. ) / 100.;
+ }
public String printHTMLTable() throws SQLException {
StringBuffer sb = new StringBuffer();
@@ -579,7 +795,7 @@
public static void main(String[] args) throws Exception {
EnrichmentEvaluation ee = new EnrichmentEvaluation();
-// ee.start();
+ ee.start();
// ee.printResultsPlain();
ee.printResultsLaTeX();
Files.createFile(new File("enrichment_eval.html"), ee.printHTMLTable());
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