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dl-learner-svn
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From: <jen...@us...> - 2011-08-03 14:49:42
|
Revision: 2981
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=2981&view=rev
Author: jenslehmann
Date: 2011-08-03 14:49:36 +0000 (Wed, 03 Aug 2011)
Log Message:
-----------
ctd. evaluation 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 2011-08-03 14:01:03 UTC (rev 2980)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-03 14:49:36 UTC (rev 2981)
@@ -1,5 +1,5 @@
/**
- * Copyright (C) 2007-2008, Jens Lehmann
+ * Copyright (C) 2007-2011, Jens Lehmann
*
* This file is part of DL-Learner.
*
@@ -19,18 +19,20 @@
*/
package org.dllearner.scripts.evaluation;
+import java.util.List;
import java.util.Set;
import java.util.TreeSet;
-import org.aksw.commons.sparql.sparqler.SelectPaginated;
-import org.aksw.commons.sparql.sparqler.Sparqler;
-import org.aksw.commons.sparql.sparqler.SparqlerHttp;
+import org.apache.log4j.Logger;
import org.dllearner.algorithms.properties.SubPropertyOfAxiomLearner;
+import org.dllearner.core.EvaluatedAxiom;
import org.dllearner.core.owl.ObjectProperty;
import org.dllearner.kb.SparqlEndpointKS;
import org.dllearner.kb.sparql.SparqlEndpoint;
+import org.dllearner.kb.sparql.SparqlQuery;
import com.hp.hpl.jena.query.QuerySolution;
+import com.hp.hpl.jena.query.ResultSet;
/**
* Evaluation of enrichment algorithms on DBpedia (Live).
@@ -40,9 +42,20 @@
*/
public class EnrichmentEvaluation {
+ private static Logger logger = Logger.getLogger(EnrichmentEvaluation.class);
+
// max. execution time for each learner for each entity
private int maxExecutionTimeInSeconds = 10;
+ // number of axioms which will be learned/considered (only applies to
+ // some learners)
+ private int nrOfAxiomsToLearn = 10;
+
+ // can be used to only evaluate a part of DBpedia
+ private int maxObjectProperties = 3;
+ private int maxDataProperties = 3;
+ private int maxClasses = 3;
+
public EnrichmentEvaluation() {
}
@@ -57,21 +70,36 @@
SparqlEndpointKS ks = new SparqlEndpointKS(se);
- SubPropertyOfAxiomLearner learner = new SubPropertyOfAxiomLearner(ks);
- learner.setMaxExecutionTimeInSeconds(10);
+ int objectProperties = 0;
+ for(ObjectProperty property : properties) {
+ SubPropertyOfAxiomLearner learner = new SubPropertyOfAxiomLearner(ks);
+ learner.setPropertyToDescribe(property);
+ learner.setMaxExecutionTimeInSeconds(10);
+ learner.start();
+ List<EvaluatedAxiom> learnedAxioms = learner.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
+ for(EvaluatedAxiom learnedAxiom : learnedAxioms) {
+ // TODO: put this in some data structure
+ System.out.println(learnedAxiom);
+ }
+ objectProperties++;
+ if(objectProperties > maxObjectProperties) {
+ break;
+ }
+ }
}
private void getAllClasses() {
-
-
-
}
private Set<ObjectProperty> getAllObjectProperties(SparqlEndpoint se) {
Set<ObjectProperty> properties = new TreeSet<ObjectProperty>();
- Sparqler x = new SparqlerHttp(se.getURL().toString());
- SelectPaginated q = new SelectPaginated(x, "PREFIX owl: <http://www.w3.org/2002/07/owl#> SELECT ?p WHERE {?p a owl:ObjectProperty}", 1000);
+ String query = "PREFIX owl: <http://www.w3.org/2002/07/owl#> SELECT ?p WHERE {?p a owl:ObjectProperty}";
+ SparqlQuery sq = new SparqlQuery(query, se);
+ // Claus' API
+// Sparqler x = new SparqlerHttp(se.getURL().toString());
+// SelectPaginated q = new SelectPaginated(x, , 1000);
+ ResultSet q = sq.send();
while(q.hasNext()) {
QuerySolution qs = q.next();
System.out.println(qs);
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jen...@us...> - 2011-08-05 13:02:52
|
Revision: 3005
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3005&view=rev
Author: jenslehmann
Date: 2011-08-05 13:02:46 +0000 (Fri, 05 Aug 2011)
Log Message:
-----------
enrichment evaluation script automatically creates SQL table
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 2011-08-05 12:54:20 UTC (rev 3004)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-05 13:02:46 UTC (rev 3005)
@@ -27,6 +27,7 @@
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.SQLException;
+import java.sql.Statement;
import java.util.LinkedList;
import java.util.List;
import java.util.Set;
@@ -34,8 +35,6 @@
import java.util.prefs.Preferences;
import org.apache.log4j.Logger;
-import org.dllearner.algorithms.properties.DisjointPropertyAxiomLearner;
-import org.dllearner.algorithms.properties.EquivalentPropertyAxiomLearner;
import org.dllearner.algorithms.properties.FunctionalPropertyAxiomLearner;
import org.dllearner.algorithms.properties.PropertyDomainAxiomLearner;
import org.dllearner.algorithms.properties.PropertyRangeAxiomLearner;
@@ -46,7 +45,6 @@
import org.dllearner.core.ComponentInitException;
import org.dllearner.core.ComponentManager;
import org.dllearner.core.EvaluatedAxiom;
-import org.dllearner.core.LearningAlgorithm;
import org.dllearner.core.config.ConfigHelper;
import org.dllearner.core.owl.ObjectProperty;
import org.dllearner.kb.SparqlEndpointKS;
@@ -119,6 +117,14 @@
String url =
"jdbc:mysql://"+dbServer+"/"+dbName;
Connection conn = DriverManager.getConnection(url, dbUser, dbPass);
+
+ Statement s = conn.createStatement ();
+ s.executeUpdate ("DROP TABLE IF EXISTS evaluation");
+ s.executeUpdate (
+ "CREATE TABLE evaluation ("
+ + "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
+ + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE)");
+ s.close();
ps = conn.prepareStatement("INSERT INTO evaluation (" +
"entity, algorithm, axiom, score ) " +
"VALUES(?,?,?,?)");
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <jen...@us...> - 2011-08-05 13:25:33
|
Revision: 3006
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3006&view=rev
Author: jenslehmann
Date: 2011-08-05 13:25:27 +0000 (Fri, 05 Aug 2011)
Log Message:
-----------
HTML output for eval script; recording runtime
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 2011-08-05 13:02:46 UTC (rev 3005)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-05 13:25:27 UTC (rev 3006)
@@ -19,6 +19,7 @@
*/
package org.dllearner.scripts.evaluation;
+import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;
@@ -26,6 +27,7 @@
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
+import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Statement;
import java.util.LinkedList;
@@ -50,6 +52,7 @@
import org.dllearner.kb.SparqlEndpointKS;
import org.dllearner.kb.sparql.SparqlEndpoint;
import org.dllearner.kb.sparql.SparqlQuery;
+import org.dllearner.utilities.Files;
import org.ini4j.IniPreferences;
import org.ini4j.InvalidFileFormatException;
@@ -60,51 +63,52 @@
* Evaluation of enrichment algorithms on DBpedia (Live).
*
* @author Jens Lehmann
- *
+ *
*/
public class EnrichmentEvaluation {
private static Logger logger = Logger.getLogger(EnrichmentEvaluation.class);
-
+
// max. execution time for each learner for each entity
private int maxExecutionTimeInSeconds = 10;
-
+
// number of axioms which will be learned/considered (only applies to
// some learners)
private int nrOfAxiomsToLearn = 10;
-
+
// can be used to only evaluate a part of DBpedia
private int maxObjectProperties = 3;
private int maxDataProperties = 3;
private int maxClasses = 3;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
-
+
+ private Connection conn;
private PreparedStatement ps;
-
+
public EnrichmentEvaluation() {
initDBConnection();
-
+
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
-// objectPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
+ // objectPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
+ // objectPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(FunctionalPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(PropertyDomainAxiomLearner.class);
objectPropertyAlgorithms.add(PropertyRangeAxiomLearner.class);
objectPropertyAlgorithms.add(SubPropertyOfAxiomLearner.class);
objectPropertyAlgorithms.add(SymmetricPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(TransitivePropertyAxiomLearner.class);
-
+
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
-// dataPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
-// dataPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
+ // dataPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
+ // dataPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(FunctionalPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(PropertyDomainAxiomLearner.class);
dataPropertyAlgorithms.add(PropertyRangeAxiomLearner.class); // ?
- dataPropertyAlgorithms.add(SubPropertyOfAxiomLearner.class);
+ dataPropertyAlgorithms.add(SubPropertyOfAxiomLearner.class);
}
-
- private void initDBConnection(){
+
+ private void initDBConnection() {
try {
String iniFile = "db_settings.ini";
Preferences prefs = new IniPreferences(new FileReader(iniFile));
@@ -112,22 +116,19 @@
String dbName = "enrichment";
String dbUser = prefs.node("database").get("user", null);
String dbPass = prefs.node("database").get("pass", null);
-
+
Class.forName("com.mysql.jdbc.Driver");
- String url =
- "jdbc:mysql://"+dbServer+"/"+dbName;
- Connection conn = DriverManager.getConnection(url, dbUser, dbPass);
-
- Statement s = conn.createStatement ();
- s.executeUpdate ("DROP TABLE IF EXISTS evaluation");
- s.executeUpdate (
- "CREATE TABLE evaluation ("
- + "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
- + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE)");
+ String url = "jdbc:mysql://" + dbServer + "/" + dbName;
+ conn = DriverManager.getConnection(url, dbUser, dbPass);
+
+ Statement s = conn.createStatement();
+ s.executeUpdate("DROP TABLE IF EXISTS evaluation");
+ s.executeUpdate("CREATE TABLE evaluation ("
+ + "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
+ + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20))");
s.close();
- ps = conn.prepareStatement("INSERT INTO evaluation (" +
- "entity, algorithm, axiom, score ) " +
- "VALUES(?,?,?,?)");
+ ps = conn.prepareStatement("INSERT INTO evaluation ("
+ + "entity, algorithm, axiom, score, runtime_ms ) " + "VALUES(?,?,?,?,?)");
} catch (ClassNotFoundException e) {
e.printStackTrace();
} catch (SQLException e) {
@@ -140,96 +141,142 @@
e.printStackTrace();
}
}
-
- private void writeToDB(
- String entity, String algorithm, String axiom, double score){
+
+ private void writeToDB(String entity, String algorithm, String axiom, double score, long runTime) {
try {
ps.setString(1, entity);
ps.setString(2, algorithm);
ps.setString(3, axiom);
ps.setDouble(4, score);
-
+ ps.setLong(5, runTime);
+
ps.executeUpdate();
} catch (SQLException e) {
- logger.error("Error while writing to DB.",e);
+ logger.error("Error while writing to DB.", e);
e.printStackTrace();
}
-
+
}
-
- public void start() throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException {
-
+
+ public void start() throws IllegalArgumentException, SecurityException, InstantiationException,
+ IllegalAccessException, InvocationTargetException, NoSuchMethodException,
+ ComponentInitException {
+
ComponentManager cm = ComponentManager.getInstance();
-
+
// create DBpedia Live knowledge source
SparqlEndpoint se = SparqlEndpoint.getEndpointDBpediaLiveAKSW();
-
+
Set<ObjectProperty> properties = getAllObjectProperties(se);
-
+
SparqlEndpointKS ks = new SparqlEndpointKS(se);
ks.init();
-
- for(Class<? extends AxiomLearningAlgorithm> algorithmClass : objectPropertyAlgorithms) {
+
+ for (Class<? extends AxiomLearningAlgorithm> algorithmClass : objectPropertyAlgorithms) {
int objectProperties = 0;
- for(ObjectProperty property : properties) {
+ for (ObjectProperty property : properties) {
// dynamically invoke constructor with SPARQL knowledge source
- AxiomLearningAlgorithm learner = algorithmClass.getConstructor(SparqlEndpointKS.class).newInstance(ks);
+ AxiomLearningAlgorithm learner = algorithmClass.getConstructor(
+ SparqlEndpointKS.class).newInstance(ks);
ConfigHelper.configure(learner, "propertyToDescribe", property.toString());
- ConfigHelper.configure(learner, "maxExecutionTimeInSeconds", maxExecutionTimeInSeconds);
+ ConfigHelper.configure(learner, "maxExecutionTimeInSeconds",
+ maxExecutionTimeInSeconds);
learner.init();
-// learner.setPropertyToDescribe(property);
-// learner.setMaxExecutionTimeInSeconds(10);
+ // learner.setPropertyToDescribe(property);
+ // learner.setMaxExecutionTimeInSeconds(10);
String algName = ComponentManager.getName(learner);
- System.out.println("Applying " + algName + " on " + property + " ... ");
+ System.out.println("Applying " + algName + " on " + property + " ... ");
+ long startTime = System.currentTimeMillis();
learner.start();
- List<EvaluatedAxiom> learnedAxioms = learner.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
- if(learnedAxioms == null) {
- writeToDB(property.toString(), algName, "NULL", 0);
+ long runTime = System.currentTimeMillis() - startTime;
+ List<EvaluatedAxiom> learnedAxioms = learner
+ .getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
+ if (learnedAxioms == null) {
+ writeToDB(property.toString(), algName, "NULL", 0, runTime);
} else {
- for(EvaluatedAxiom learnedAxiom : learnedAxioms) {
- writeToDB(property.toString(), algName, learnedAxiom.getAxiom().toString(), learnedAxiom.getScore().getAccuracy());
+ for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
+ double score = learnedAxiom.getScore().getAccuracy();
+ if (Double.isNaN(score)) {
+ score = -1;
+ }
+ writeToDB(property.toString(), algName, learnedAxiom.getAxiom().toString(),
+ score, runTime);
}
}
objectProperties++;
- if(objectProperties > maxObjectProperties) {
+ if (objectProperties > maxObjectProperties) {
break;
}
}
- }
-
+ }
+
}
-
+
private void getAllClasses() {
}
-
+
private Set<ObjectProperty> getAllObjectProperties(SparqlEndpoint se) {
Set<ObjectProperty> properties = new TreeSet<ObjectProperty>();
String query = "PREFIX owl: <http://www.w3.org/2002/07/owl#> SELECT ?p WHERE {?p a owl:ObjectProperty}";
SparqlQuery sq = new SparqlQuery(query, se);
// Claus' API
-// Sparqler x = new SparqlerHttp(se.getURL().toString());
-// SelectPaginated q = new SelectPaginated(x, , 1000);
+ // Sparqler x = new SparqlerHttp(se.getURL().toString());
+ // SelectPaginated q = new SelectPaginated(x, , 1000);
ResultSet q = sq.send();
- while(q.hasNext()) {
- QuerySolution qs = q.next();
- properties.add(new ObjectProperty(qs.getResource("p").getURI()));
- }
- return properties;
+ while (q.hasNext()) {
+ QuerySolution qs = q.next();
+ properties.add(new ObjectProperty(qs.getResource("p").getURI()));
+ }
+ return properties;
}
-
+
public void printResultsPlain() {
-
+
}
-
+
public void printResultsLaTeX() {
-
+
}
-
- public static void main(String[] args) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException {
+
+ public String printHTMLTable() throws SQLException {
+ StringBuffer sb = new StringBuffer();
+ Statement s = conn.createStatement();
+ s.executeQuery("SELECT * FROM evaluation");
+ java.sql.ResultSet rs = s.getResultSet();
+
+ ResultSetMetaData md = rs.getMetaData();
+ int count = md.getColumnCount();
+ sb.append("<table border=1>");
+ sb.append("<tr>");
+ for (int i = 1; i <= count; i++) {
+ sb.append("<th>");
+ sb.append(md.getColumnLabel(i));
+ sb.append("</th>");
+ }
+ sb.append("</tr>");
+ while (rs.next()) {
+ sb.append("<tr>");
+ for (int i = 1; i <= count; i++) {
+ sb.append("<td>");
+ sb.append(rs.getString(i));
+ sb.append("</td>");
+ }
+ sb.append("</tr>");
+ }
+ sb.append("</table>");
+ rs.close();
+ s.close();
+ return sb.toString();
+ }
+
+ public static void main(String[] args) throws IllegalArgumentException, SecurityException,
+ InstantiationException, IllegalAccessException, InvocationTargetException,
+ NoSuchMethodException, ComponentInitException, SQLException {
EnrichmentEvaluation ee = new EnrichmentEvaluation();
ee.start();
- ee.printResultsPlain();
+ // ee.printResultsPlain();
+ Files.createFile(new File("enrichment_eval.html"), ee.printHTMLTable());
}
-
+
}
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|
|
From: <jen...@us...> - 2011-08-05 13:47:14
|
Revision: 3009
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3009&view=rev
Author: jenslehmann
Date: 2011-08-05 13:47:08 +0000 (Fri, 05 Aug 2011)
Log Message:
-----------
improved eval script output
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 2011-08-05 13:33:42 UTC (rev 3008)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-05 13:47:08 UTC (rev 3009)
@@ -30,8 +30,10 @@
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Statement;
+import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
+import java.util.Map;
import java.util.Set;
import java.util.TreeSet;
import java.util.prefs.Preferences;
@@ -83,12 +85,19 @@
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
+ private String baseURI = "http://dbpedia.org/resource/";
+ private Map<String,String> prefixes;
+
private Connection conn;
private PreparedStatement ps;
public EnrichmentEvaluation() {
initDBConnection();
+ prefixes = new HashMap<String,String>();
+ prefixes.put("dbp","http://dbpedia.org/property/");
+ prefixes.put("dbo","http://dbpedia.org/ontology/");
+
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
// objectPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
// objectPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
@@ -193,14 +202,14 @@
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
if (learnedAxioms == null) {
- writeToDB(property.toString(), algName, "NULL", 0, runTime);
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime);
} else {
for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
double score = learnedAxiom.getScore().getAccuracy();
if (Double.isNaN(score)) {
score = -1;
}
- writeToDB(property.toString(), algName, learnedAxiom.getAxiom().toString(),
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
score, runTime);
}
}
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|
|
From: <lor...@us...> - 2011-08-08 06:01:33
|
Revision: 3011
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3011&view=rev
Author: lorenz_b
Date: 2011-08-08 06:01:26 +0000 (Mon, 08 Aug 2011)
Log Message:
-----------
Small modification.
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 2011-08-08 06:00:16 UTC (rev 3010)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-08 06:01:26 UTC (rev 3011)
@@ -24,6 +24,8 @@
import java.io.FileReader;
import java.io.IOException;
import java.lang.reflect.InvocationTargetException;
+import java.net.SocketException;
+import java.net.SocketTimeoutException;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
@@ -39,6 +41,7 @@
import java.util.prefs.Preferences;
import org.apache.log4j.Logger;
+import org.dllearner.algorithms.properties.EquivalentPropertyAxiomLearner;
import org.dllearner.algorithms.properties.FunctionalPropertyAxiomLearner;
import org.dllearner.algorithms.properties.PropertyDomainAxiomLearner;
import org.dllearner.algorithms.properties.PropertyRangeAxiomLearner;
@@ -79,7 +82,7 @@
private int nrOfAxiomsToLearn = 10;
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 3;
+ private int maxObjectProperties = 0;
private int maxDataProperties = 3;
private int maxClasses = 3;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
@@ -100,7 +103,7 @@
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
// objectPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
- // objectPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(FunctionalPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(PropertyDomainAxiomLearner.class);
objectPropertyAlgorithms.add(PropertyRangeAxiomLearner.class);
@@ -122,7 +125,7 @@
String iniFile = "db_settings.ini";
Preferences prefs = new IniPreferences(new FileReader(iniFile));
String dbServer = prefs.node("database").get("server", null);
- String dbName = "enrichment";
+ String dbName = prefs.node("database").get("name", null);
String dbUser = prefs.node("database").get("user", null);
String dbPass = prefs.node("database").get("pass", null);
@@ -170,7 +173,7 @@
public void start() throws IllegalArgumentException, SecurityException, InstantiationException,
IllegalAccessException, InvocationTargetException, NoSuchMethodException,
ComponentInitException {
-
+ long overallStartTime = System.currentTimeMillis();
ComponentManager cm = ComponentManager.getInstance();
// create DBpedia Live knowledge source
@@ -197,11 +200,20 @@
String algName = ComponentManager.getName(learner);
System.out.println("Applying " + algName + " on " + property + " ... ");
long startTime = System.currentTimeMillis();
- learner.start();
+ boolean timeout = false;
+ try {
+ learner.start();
+ } catch (Exception e) {
+ if(e.getCause() instanceof SocketTimeoutException){
+ timeout = true;
+ }
+ }
long runTime = System.currentTimeMillis() - startTime;
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
- if (learnedAxioms == null) {
+ if(timeout){
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
+ } else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime);
} else {
for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
@@ -219,6 +231,7 @@
}
}
}
+ System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
}
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|
|
From: <lor...@us...> - 2011-08-10 06:53:26
|
Revision: 3021
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3021&view=rev
Author: lorenz_b
Date: 2011-08-10 06:53:19 +0000 (Wed, 10 Aug 2011)
Log Message:
-----------
Extended evaluation 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 2011-08-09 15:47:19 UTC (rev 3020)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-10 06:53:19 UTC (rev 3021)
@@ -24,7 +24,6 @@
import java.io.FileReader;
import java.io.IOException;
import java.lang.reflect.InvocationTargetException;
-import java.net.SocketException;
import java.net.SocketTimeoutException;
import java.sql.Connection;
import java.sql.DriverManager;
@@ -41,13 +40,20 @@
import java.util.prefs.Preferences;
import org.apache.log4j.Logger;
+import org.dllearner.algorithms.properties.DataPropertyDomainAxiomLearner;
+import org.dllearner.algorithms.properties.DataPropertyRangeAxiomLearner;
+import org.dllearner.algorithms.properties.DisjointDataPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.DisjointPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.EquivalentDataPropertyAxiomLearner;
import org.dllearner.algorithms.DisjointClassesLearner;
import org.dllearner.algorithms.SimpleSubclassLearner;
import org.dllearner.algorithms.celoe.CELOE;
import org.dllearner.algorithms.properties.EquivalentPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.FunctionalDataPropertyAxiomLearner;
import org.dllearner.algorithms.properties.FunctionalPropertyAxiomLearner;
import org.dllearner.algorithms.properties.PropertyDomainAxiomLearner;
import org.dllearner.algorithms.properties.PropertyRangeAxiomLearner;
+import org.dllearner.algorithms.properties.SubDataPropertyOfAxiomLearner;
import org.dllearner.algorithms.properties.SubPropertyOfAxiomLearner;
import org.dllearner.algorithms.properties.SymmetricPropertyAxiomLearner;
import org.dllearner.algorithms.properties.TransitivePropertyAxiomLearner;
@@ -57,6 +63,8 @@
import org.dllearner.core.EvaluatedAxiom;
import org.dllearner.core.LearningAlgorithm;
import org.dllearner.core.config.ConfigHelper;
+import org.dllearner.core.owl.DatatypeProperty;
+import org.dllearner.core.owl.NamedClass;
import org.dllearner.core.owl.ObjectProperty;
import org.dllearner.kb.SparqlEndpointKS;
import org.dllearner.kb.sparql.SparqlEndpoint;
@@ -86,9 +94,9 @@
private int nrOfAxiomsToLearn = 10;
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 0;
- private int maxDataProperties = 3;
- private int maxClasses = 3;
+ private int maxObjectProperties = 10;
+ private int maxDataProperties = 10;
+ private int maxClasses = 10;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -107,7 +115,7 @@
prefixes.put("dbo","http://dbpedia.org/ontology/");
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
- // objectPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(FunctionalPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(PropertyDomainAxiomLearner.class);
@@ -117,12 +125,12 @@
objectPropertyAlgorithms.add(TransitivePropertyAxiomLearner.class);
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
- // dataPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
- // dataPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
- dataPropertyAlgorithms.add(FunctionalPropertyAxiomLearner.class);
- dataPropertyAlgorithms.add(PropertyDomainAxiomLearner.class);
- dataPropertyAlgorithms.add(PropertyRangeAxiomLearner.class); // ?
- dataPropertyAlgorithms.add(SubPropertyOfAxiomLearner.class);
+ dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
+ dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
+ dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
+ dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
+ dataPropertyAlgorithms.add(DataPropertyRangeAxiomLearner.class);
+ dataPropertyAlgorithms.add(SubDataPropertyOfAxiomLearner.class);
classAlgorithms = new LinkedList<Class<? extends LearningAlgorithm>>();
classAlgorithms.add(DisjointClassesLearner.class);
@@ -189,11 +197,20 @@
// create DBpedia Live knowledge source
SparqlEndpoint se = SparqlEndpoint.getEndpointDBpediaLiveAKSW();
- Set<ObjectProperty> properties = getAllObjectProperties(se);
-
SparqlEndpointKS ks = new SparqlEndpointKS(se);
ks.init();
+
+ evaluateObjectProperties(ks);
+
+ evaluateDataProperties(ks);
+
+ System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
+ }
+
+ private void evaluateObjectProperties(SparqlEndpointKS ks)throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ Set<ObjectProperty> properties = getAllObjectProperties(ks.getEndpoint());
+
for (Class<? extends AxiomLearningAlgorithm> algorithmClass : objectPropertyAlgorithms) {
int objectProperties = 0;
for (ObjectProperty property : properties) {
@@ -241,11 +258,58 @@
}
}
}
- System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
-
}
+
+ private void evaluateDataProperties(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ Set<DatatypeProperty> properties = getAllDataProperties(ks.getEndpoint());
- private void getAllClasses() {
+ for (Class<? extends AxiomLearningAlgorithm> algorithmClass : dataPropertyAlgorithms) {
+ int dataProperties = 0;
+ for (DatatypeProperty property : properties) {
+
+ // dynamically invoke constructor with SPARQL knowledge source
+ AxiomLearningAlgorithm learner = algorithmClass.getConstructor(
+ SparqlEndpointKS.class).newInstance(ks);
+ ConfigHelper.configure(learner, "propertyToDescribe", property.toString());
+ ConfigHelper.configure(learner, "maxExecutionTimeInSeconds",
+ maxExecutionTimeInSeconds);
+ learner.init();
+ // learner.setPropertyToDescribe(property);
+ // learner.setMaxExecutionTimeInSeconds(10);
+ String algName = ComponentManager.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;
+ }
+ }
+ long runTime = System.currentTimeMillis() - startTime;
+ List<EvaluatedAxiom> learnedAxioms = learner
+ .getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
+ if(timeout){
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
+ } else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime);
+ } else {
+ for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
+ double score = learnedAxiom.getScore().getAccuracy();
+ if (Double.isNaN(score)) {
+ score = -1;
+ }
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
+ score, runTime);
+ }
+ }
+ dataProperties++;
+ if (maxDataProperties != 0 && dataProperties > maxDataProperties) {
+ break;
+ }
+ }
+ }
}
private Set<ObjectProperty> getAllObjectProperties(SparqlEndpoint se) {
@@ -262,6 +326,36 @@
}
return properties;
}
+
+ private Set<DatatypeProperty> getAllDataProperties(SparqlEndpoint se) {
+ Set<DatatypeProperty> properties = new TreeSet<DatatypeProperty>();
+ String query = "PREFIX owl: <http://www.w3.org/2002/07/owl#> SELECT ?p WHERE {?p a owl:DatatypeProperty}";
+ SparqlQuery sq = new SparqlQuery(query, se);
+ // Claus' API
+ // Sparqler x = new SparqlerHttp(se.getURL().toString());
+ // SelectPaginated q = new SelectPaginated(x, , 1000);
+ ResultSet q = sq.send();
+ while (q.hasNext()) {
+ QuerySolution qs = q.next();
+ properties.add(new DatatypeProperty(qs.getResource("p").getURI()));
+ }
+ return properties;
+ }
+
+ private Set<NamedClass> getAllClasses(SparqlEndpoint se) {
+ Set<NamedClass> classes = new TreeSet<NamedClass>();
+ String query = "PREFIX owl: <http://www.w3.org/2002/07/owl#> SELECT ?c WHERE {?c a owl:Class}";
+ SparqlQuery sq = new SparqlQuery(query, se);
+ // Claus' API
+ // Sparqler x = new SparqlerHttp(se.getURL().toString());
+ // SelectPaginated q = new SelectPaginated(x, , 1000);
+ ResultSet q = sq.send();
+ while (q.hasNext()) {
+ QuerySolution qs = q.next();
+ classes.add(new NamedClass(qs.getResource("c").getURI()));
+ }
+ return classes;
+ }
public void printResultsPlain() {
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <lor...@us...> - 2011-08-11 06:40:43
|
Revision: 3026
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3026&view=rev
Author: lorenz_b
Date: 2011-08-11 06:40:37 +0000 (Thu, 11 Aug 2011)
Log Message:
-----------
Changes in eval script needed because of refactoring.
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 2011-08-10 14:19:01 UTC (rev 3025)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-11 06:40:37 UTC (rev 3026)
@@ -40,23 +40,24 @@
import java.util.prefs.Preferences;
import org.apache.log4j.Logger;
+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.DisjointPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.DisjointObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.EquivalentDataPropertyAxiomLearner;
-import org.dllearner.algorithms.DisjointClassesLearner;
-import org.dllearner.algorithms.SimpleSubclassLearner;
-import org.dllearner.algorithms.celoe.CELOE;
-import org.dllearner.algorithms.properties.EquivalentPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.EquivalentObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.FunctionalDataPropertyAxiomLearner;
-import org.dllearner.algorithms.properties.FunctionalPropertyAxiomLearner;
-import org.dllearner.algorithms.properties.PropertyDomainAxiomLearner;
+import org.dllearner.algorithms.properties.FunctionalObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.InverseFunctionalObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.ObjectPropertyDomainAxiomLearner;
import org.dllearner.algorithms.properties.PropertyRangeAxiomLearner;
import org.dllearner.algorithms.properties.SubDataPropertyOfAxiomLearner;
-import org.dllearner.algorithms.properties.SubPropertyOfAxiomLearner;
-import org.dllearner.algorithms.properties.SymmetricPropertyAxiomLearner;
-import org.dllearner.algorithms.properties.TransitivePropertyAxiomLearner;
+import org.dllearner.algorithms.properties.SubObjectPropertyOfAxiomLearner;
+import org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner;
import org.dllearner.core.AxiomLearningAlgorithm;
import org.dllearner.core.ComponentInitException;
import org.dllearner.core.ComponentManager;
@@ -115,14 +116,15 @@
prefixes.put("dbo","http://dbpedia.org/ontology/");
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
- objectPropertyAlgorithms.add(DisjointPropertyAxiomLearner.class);
- objectPropertyAlgorithms.add(EquivalentPropertyAxiomLearner.class);
- objectPropertyAlgorithms.add(FunctionalPropertyAxiomLearner.class);
- objectPropertyAlgorithms.add(PropertyDomainAxiomLearner.class);
+ objectPropertyAlgorithms.add(DisjointObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(EquivalentObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(FunctionalObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(InverseFunctionalObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(ObjectPropertyDomainAxiomLearner.class);
objectPropertyAlgorithms.add(PropertyRangeAxiomLearner.class);
- objectPropertyAlgorithms.add(SubPropertyOfAxiomLearner.class);
- objectPropertyAlgorithms.add(SymmetricPropertyAxiomLearner.class);
- objectPropertyAlgorithms.add(TransitivePropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(SubObjectPropertyOfAxiomLearner.class);
+ objectPropertyAlgorithms.add(SymmetricObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(TransitiveObjectPropertyAxiomLearner.class);
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <lor...@us...> - 2011-08-11 14:33:53
|
Revision: 3035
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3035&view=rev
Author: lorenz_b
Date: 2011-08-11 14:33:45 +0000 (Thu, 11 Aug 2011)
Log Message:
-----------
Small changes.
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 2011-08-11 14:31:41 UTC (rev 3034)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-11 14:33:45 UTC (rev 3035)
@@ -137,6 +137,8 @@
classAlgorithms.add(DisjointClassesLearner.class);
classAlgorithms.add(SimpleSubclassLearner.class);
classAlgorithms.add(CELOE.class);
+
+ initDBConnection();
}
private void initDBConnection() {
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <lor...@us...> - 2011-08-19 11:12:58
|
Revision: 3072
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3072&view=rev
Author: lorenz_b
Date: 2011-08-19 11:12:51 +0000 (Fri, 19 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 2011-08-19 11:07:00 UTC (rev 3071)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-19 11:12:51 UTC (rev 3072)
@@ -19,12 +19,16 @@
*/
package org.dllearner.scripts.evaluation;
+import java.io.BufferedInputStream;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;
+import java.io.InputStream;
import java.lang.reflect.InvocationTargetException;
+import java.net.MalformedURLException;
import java.net.SocketTimeoutException;
+import java.net.URL;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
@@ -38,14 +42,14 @@
import java.util.Set;
import java.util.prefs.Preferences;
+import org.apache.commons.compress.compressors.CompressorException;
+import org.apache.commons.compress.compressors.CompressorInputStream;
+import org.apache.commons.compress.compressors.CompressorStreamFactory;
import org.apache.log4j.FileAppender;
import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.log4j.SimpleLayout;
-import org.apache.velocity.context.EvaluateContext;
-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;
@@ -57,19 +61,17 @@
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;
-import org.dllearner.algorithms.properties.ObjectPropertyRangeAxiomLearner;
import org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner;
import org.dllearner.core.AbstractCELA;
import org.dllearner.core.AnnComponentManager;
import org.dllearner.core.AxiomLearningAlgorithm;
import org.dllearner.core.ComponentInitException;
-import org.dllearner.core.ComponentManager;
import org.dllearner.core.EvaluatedAxiom;
-import org.dllearner.core.EvaluatedDescription;
import org.dllearner.core.LearningAlgorithm;
import org.dllearner.core.config.ConfigHelper;
import org.dllearner.core.owl.DatatypeProperty;
@@ -80,9 +82,18 @@
import org.dllearner.kb.sparql.SparqlEndpoint;
import org.dllearner.utilities.CommonPrefixMap;
import org.dllearner.utilities.Files;
+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.OWLAxiom;
+import org.semanticweb.owlapi.model.OWLOntology;
+import org.semanticweb.owlapi.model.OWLOntologyCreationException;
+import org.semanticweb.owlapi.reasoner.InferenceType;
+import org.semanticweb.owlapi.reasoner.OWLReasoner;
+import com.clarkparsia.pellet.owlapiv3.PelletReasonerFactory;
+
/**
* Evaluation of enrichment algorithms on DBpedia (Live).
*
@@ -103,7 +114,7 @@
// can be used to only evaluate a part of DBpedia
private int maxObjectProperties = 0;
private int maxDataProperties = 0;
- private int maxClasses = 0;
+ private int maxClasses = 10;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -113,6 +124,9 @@
private Connection conn;
private PreparedStatement ps;
+
+ private OWLOntology dbPediaOntology;
+ private OWLReasoner reasoner;
public EnrichmentEvaluation() {
@@ -121,25 +135,25 @@
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(SubObjectPropertyOfAxiomLearner.class);
- objectPropertyAlgorithms.add(SymmetricObjectPropertyAxiomLearner.class);
- objectPropertyAlgorithms.add(TransitiveObjectPropertyAxiomLearner.class);
- objectPropertyAlgorithms.add(IrreflexiveObjectPropertyAxiomLearner.class);
- objectPropertyAlgorithms.add(ReflexiveObjectPropertyAxiomLearner.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);
+// objectPropertyAlgorithms.add(IrreflexiveObjectPropertyAxiomLearner.class);
+// objectPropertyAlgorithms.add(ReflexiveObjectPropertyAxiomLearner.class);
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
- dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
- dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
- dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
- dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
- dataPropertyAlgorithms.add(DataPropertyRangeAxiomLearner.class);
- dataPropertyAlgorithms.add(SubDataPropertyOfAxiomLearner.class);
+// dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
+// dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
+// dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
+// dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
+// dataPropertyAlgorithms.add(DataPropertyRangeAxiomLearner.class);
+// dataPropertyAlgorithms.add(SubDataPropertyOfAxiomLearner.class);
classAlgorithms = new LinkedList<Class<? extends LearningAlgorithm>>();
// classAlgorithms.add(DisjointClassesLearner.class);
@@ -147,6 +161,7 @@
// classAlgorithms.add(CELOE.class);
initDBConnection();
+ loadDBpediaOntology();
}
private void initDBConnection() {
@@ -381,7 +396,7 @@
double score = learnedAxiom.getScore().getAccuracy();
if (Double.isNaN(score)) {
score = -1;
- }
+ }System.out.println(learnedAxiom.getAxiom().toManchesterSyntaxString(algName, prefixes) + ":" + isEntailed(learnedAxiom));
writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
score, runTime);
}
@@ -440,10 +455,36 @@
s.close();
return sb.toString();
}
+
+ private boolean isEntailed(EvaluatedAxiom evalAxiom){
+ OWLAxiom axiom = OWLAPIConverter.getOWLAPIAxiom(evalAxiom.getAxiom());
+ boolean entailed = reasoner.isEntailed(axiom);
+ return entailed;
+ }
+
+ /**
+ * Loads DBpedia ontology from remote URL and initializes the reasoner.
+ */
+ private void loadDBpediaOntology(){
+ try {
+ URL url = new URL("http://downloads.dbpedia.org/3.6/dbpedia_3.6.owl.bz2");
+ InputStream is = new BufferedInputStream(url.openStream());
+ CompressorInputStream in = new CompressorStreamFactory().createCompressorInputStream("bzip2", is);
+ dbPediaOntology = OWLManager.createOWLOntologyManager().loadOntologyFromOntologyDocument(in);
+ reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
+ reasoner.precomputeInferences(InferenceType.CLASS_HIERARCHY);
+ } catch (MalformedURLException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (CompressorException e) {
+ e.printStackTrace();
+ } catch (OWLOntologyCreationException e) {
+ e.printStackTrace();
+ }
+ }
- public static void main(String[] args) throws IllegalArgumentException, SecurityException,
- InstantiationException, IllegalAccessException, InvocationTargetException,
- NoSuchMethodException, ComponentInitException, SQLException, IOException {
+ public static void main(String[] args) throws Exception {
EnrichmentEvaluation ee = new EnrichmentEvaluation();
ee.start();
// ee.printResultsPlain();
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|
|
From: <lor...@us...> - 2011-08-22 13:54:48
|
Revision: 3091
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3091&view=rev
Author: lorenz_b
Date: 2011-08-22 13:54:38 +0000 (Mon, 22 Aug 2011)
Log Message:
-----------
Added column in evaluation database table, which declares whether the learned axiom was entailed by the knowledge base before.
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 2011-08-22 13:21:34 UTC (rev 3090)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-22 13:54:38 UTC (rev 3091)
@@ -35,6 +35,7 @@
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Statement;
+import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
@@ -72,6 +73,7 @@
import org.dllearner.core.AxiomLearningAlgorithm;
import org.dllearner.core.ComponentInitException;
import org.dllearner.core.EvaluatedAxiom;
+import org.dllearner.core.EvaluatedDescription;
import org.dllearner.core.LearningAlgorithm;
import org.dllearner.core.config.ConfigHelper;
import org.dllearner.core.owl.DatatypeProperty;
@@ -80,6 +82,7 @@
import org.dllearner.kb.SparqlEndpointKS;
import org.dllearner.kb.sparql.SPARQLTasks;
import org.dllearner.kb.sparql.SparqlEndpoint;
+import org.dllearner.learningproblems.AxiomScore;
import org.dllearner.utilities.CommonPrefixMap;
import org.dllearner.utilities.Files;
import org.dllearner.utilities.owl.OWLAPIConverter;
@@ -181,10 +184,10 @@
s.executeUpdate("DROP TABLE IF EXISTS evaluation");
s.executeUpdate("CREATE TABLE evaluation ("
+ "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
- + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20))");
+ + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20), entailed BOOLEAN)");
s.close();
ps = conn.prepareStatement("INSERT INTO evaluation ("
- + "entity, algorithm, axiom, score, runtime_ms ) " + "VALUES(?,?,?,?,?)");
+ + "entity, algorithm, axiom, score, runtime_ms, entailed ) " + "VALUES(?,?,?,?,?,?)");
} catch (ClassNotFoundException e) {
e.printStackTrace();
} catch (SQLException e) {
@@ -198,13 +201,14 @@
}
}
- private void writeToDB(String entity, String algorithm, String axiom, double score, long runTime) {
+ private void writeToDB(String entity, String algorithm, String axiom, double score, long runTime, boolean entailed) {
try {
ps.setString(1, entity);
ps.setString(2, algorithm);
ps.setString(3, axiom);
ps.setDouble(4, score);
ps.setLong(5, runTime);
+ ps.setBoolean(6, entailed);
ps.executeUpdate();
} catch (SQLException e) {
@@ -270,9 +274,9 @@
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
if(timeout){
- writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
+ 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);
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
} else {
for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
double score = learnedAxiom.getScore().getAccuracy();
@@ -280,7 +284,7 @@
score = -1;
}
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
- score, runTime);
+ score, runTime, isEntailed(learnedAxiom));
}
}
objectProperties++;
@@ -330,9 +334,9 @@
List<EvaluatedAxiom> learnedAxioms = learner
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
if(timeout){
- writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
+ 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);
+ writeToDB(property.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
} else {
for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
double score = learnedAxiom.getScore().getAccuracy();
@@ -340,7 +344,7 @@
score = -1;
}
writeToDB(property.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
- score, runTime);
+ score, runTime, isEntailed(learnedAxiom));
}
}
dataProperties++;
@@ -384,25 +388,31 @@
}
}
long runTime = System.currentTimeMillis() - startTime;
+ List<EvaluatedAxiom> learnedAxioms = null;
if(learner instanceof AxiomLearningAlgorithm){
- List<EvaluatedAxiom> learnedAxioms = ((AxiomLearningAlgorithm)learner)
+ learnedAxioms = ((AxiomLearningAlgorithm)learner)
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
- if(timeout){
- writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime);
- } else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
- writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime);
- } else {
- for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
- double score = learnedAxiom.getScore().getAccuracy();
- if (Double.isNaN(score)) {
- score = -1;
- }System.out.println(learnedAxiom.getAxiom().toManchesterSyntaxString(algName, prefixes) + ":" + isEntailed(learnedAxiom));
- writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
- score, runTime);
+ } else if(learner instanceof AbstractCELA){
+ learnedAxioms = new ArrayList<EvaluatedAxiom>();
+ List<? extends EvaluatedDescription> learnedDescriptions = ((AbstractCELA)learner).getCurrentlyBestEvaluatedDescriptions(nrOfAxiomsToLearn);
+ for(EvaluatedDescription learnedDescription : learnedDescriptions){
+ //TODO create axioms and differ between equivalent and subclass mode in CELOE
+ learnedAxioms.add(new EvaluatedAxiom(null, new AxiomScore(learnedDescription.getAccuracy())));
+ }
+ }
+ if(timeout){
+ writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
+ } else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
+ writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
+ } else {
+ for (EvaluatedAxiom learnedAxiom : learnedAxioms) {
+ double score = learnedAxiom.getScore().getAccuracy();
+ if (Double.isNaN(score)) {
+ score = -1;
}
+ writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes) .toString(), algName, learnedAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes),
+ score, runTime, isEntailed(learnedAxiom));
}
- } else if(learner instanceof AbstractCELA){
-
}
classesCnt++;
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|
|
From: <lor...@us...> - 2011-08-23 14:48:08
|
Revision: 3101
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3101&view=rev
Author: lorenz_b
Date: 2011-08-23 14:48:02 +0000 (Tue, 23 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 2011-08-23 13:12:01 UTC (rev 3100)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-23 14:48:02 UTC (rev 3101)
@@ -50,27 +50,12 @@
import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.log4j.SimpleLayout;
+import org.dllearner.algorithms.DisjointClassesLearner;
import org.dllearner.algorithms.SimpleSubclassLearner;
-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;
-import org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner;
-import org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner;
import org.dllearner.core.AbstractCELA;
import org.dllearner.core.AnnComponentManager;
import org.dllearner.core.AxiomLearningAlgorithm;
+import org.dllearner.core.ComponentAnn;
import org.dllearner.core.ComponentInitException;
import org.dllearner.core.EvaluatedAxiom;
import org.dllearner.core.EvaluatedDescription;
@@ -159,7 +144,7 @@
// dataPropertyAlgorithms.add(SubDataPropertyOfAxiomLearner.class);
classAlgorithms = new LinkedList<Class<? extends LearningAlgorithm>>();
-// classAlgorithms.add(DisjointClassesLearner.class);
+ classAlgorithms.add(DisjointClassesLearner.class);
classAlgorithms.add(SimpleSubclassLearner.class);
// classAlgorithms.add(CELOE.class);
@@ -181,11 +166,11 @@
conn = DriverManager.getConnection(url, dbUser, dbPass);
Statement s = conn.createStatement();
- s.executeUpdate("DROP TABLE IF EXISTS evaluation");
- s.executeUpdate("CREATE TABLE evaluation ("
- + "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
- + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20), entailed BOOLEAN)");
- s.close();
+// s.executeUpdate("DROP TABLE IF EXISTS evaluation");
+// s.executeUpdate("CREATE TABLE evaluation ("
+// + "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
+// + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20), entailed BOOLEAN)");
+// s.close();
ps = conn.prepareStatement("INSERT INTO evaluation ("
+ "entity, algorithm, axiom, score, runtime_ms, entailed ) " + "VALUES(?,?,?,?,?,?)");
} catch (ClassNotFoundException e) {
@@ -431,8 +416,31 @@
}
- public void printResultsLaTeX() {
-
+ public void printResultsLaTeX() throws Exception{
+ double threshold = 0.9;
+
+ for(Class<? extends LearningAlgorithm> algo : classAlgorithms){
+ String algoName = algo.getAnnotation(ComponentAnn.class).name();
+ //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.setString(1, algoName);
+ ps.setDouble(2, threshold);
+ java.sql.ResultSet rs = ps.executeQuery();
+ rs.next();System.out.println(rs.getDouble(1));
+ int avgSuggestionsAboveThreshold = rs.getInt(1);
+
+ //compute average runtime
+ ps = conn.prepareStatement("SELECT AVG(runtime_ms) FROM evaluation WHERE algorithm=?");
+ ps.setString(1, algoName);
+ rs = ps.executeQuery();
+ rs.next();
+ int avgRuntimeInMilliseconds = rs.getInt(1);
+
+ //compute
+
+ System.out.println(algoName + "-" + avgSuggestionsAboveThreshold + "-" + avgRuntimeInMilliseconds);
+ }
+
}
public String printHTMLTable() throws SQLException {
@@ -496,8 +504,9 @@
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());
FileAppender app = new FileAppender(new SimpleLayout(), "log/enrichmentEvalErrors.log");
Logger.getRootLogger().setLevel(Level.ERROR);
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|
|
From: <lor...@us...> - 2011-08-24 09:14:17
|
Revision: 3107
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3107&view=rev
Author: lorenz_b
Date: 2011-08-24 09:14:11 +0000 (Wed, 24 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 2011-08-24 04:53:25 UTC (rev 3106)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-24 09:14:11 UTC (rev 3107)
@@ -52,6 +52,15 @@
import org.apache.log4j.SimpleLayout;
import org.dllearner.algorithms.DisjointClassesLearner;
import org.dllearner.algorithms.SimpleSubclassLearner;
+import org.dllearner.algorithms.properties.DataPropertyDomainAxiomLearner;
+import org.dllearner.algorithms.properties.DataPropertyRangeAxiomLearner;
+import org.dllearner.algorithms.properties.FunctionalDataPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.IrreflexiveObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.ReflexiveObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.SubDataPropertyOfAxiomLearner;
+import org.dllearner.algorithms.properties.SubObjectPropertyOfAxiomLearner;
+import org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner;
+import org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner;
import org.dllearner.core.AbstractCELA;
import org.dllearner.core.AnnComponentManager;
import org.dllearner.core.AxiomLearningAlgorithm;
@@ -100,9 +109,9 @@
private int nrOfAxiomsToLearn = 10;
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 0;
- private int maxDataProperties = 0;
- private int maxClasses = 10;
+ private int maxObjectProperties = 3;
+ private int maxDataProperties = 3;
+ private int maxClasses = 3;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -129,19 +138,19 @@
// objectPropertyAlgorithms.add(InverseFunctionalObjectPropertyAxiomLearner.class);
// objectPropertyAlgorithms.add(ObjectPropertyDomainAxiomLearner.class);
// objectPropertyAlgorithms.add(ObjectPropertyRangeAxiomLearner.class);
-// objectPropertyAlgorithms.add(SubObjectPropertyOfAxiomLearner.class);
-// objectPropertyAlgorithms.add(SymmetricObjectPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(TransitiveObjectPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(IrreflexiveObjectPropertyAxiomLearner.class);
-// objectPropertyAlgorithms.add(ReflexiveObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(SubObjectPropertyOfAxiomLearner.class);
+ objectPropertyAlgorithms.add(SymmetricObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(TransitiveObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(IrreflexiveObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(ReflexiveObjectPropertyAxiomLearner.class);
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
// dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
// dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
-// dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
-// dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
-// dataPropertyAlgorithms.add(DataPropertyRangeAxiomLearner.class);
-// dataPropertyAlgorithms.add(SubDataPropertyOfAxiomLearner.class);
+ dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
+ dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
+ dataPropertyAlgorithms.add(DataPropertyRangeAxiomLearner.class);
+ dataPropertyAlgorithms.add(SubDataPropertyOfAxiomLearner.class);
classAlgorithms = new LinkedList<Class<? extends LearningAlgorithm>>();
classAlgorithms.add(DisjointClassesLearner.class);
@@ -165,14 +174,6 @@
String url = "jdbc:mysql://" + dbServer + "/" + dbName;
conn = DriverManager.getConnection(url, dbUser, dbPass);
- Statement s = conn.createStatement();
-// s.executeUpdate("DROP TABLE IF EXISTS evaluation");
-// s.executeUpdate("CREATE TABLE evaluation ("
-// + "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
-// + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20), entailed BOOLEAN)");
-// s.close();
- ps = conn.prepareStatement("INSERT INTO evaluation ("
- + "entity, algorithm, axiom, score, runtime_ms, entailed ) " + "VALUES(?,?,?,?,?,?)");
} catch (ClassNotFoundException e) {
e.printStackTrace();
} catch (SQLException e) {
@@ -185,6 +186,22 @@
e.printStackTrace();
}
}
+
+ private void dropAndCreateTable(){
+ try {
+ Statement s = conn.createStatement();
+ s.executeUpdate("DROP TABLE IF EXISTS evaluation");
+ s.executeUpdate("CREATE TABLE evaluation ("
+ + "id INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY,"
+ + "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20), entailed BOOLEAN)");
+ s.close();
+
+ ps = conn.prepareStatement("INSERT INTO evaluation ("
+ + "entity, algorithm, axiom, score, runtime_ms, entailed ) " + "VALUES(?,?,?,?,?,?)");
+ } catch (SQLException e) {
+ e.printStackTrace();
+ }
+ }
private void writeToDB(String entity, String algorithm, String axiom, double score, long runTime, boolean entailed) {
try {
@@ -206,6 +223,8 @@
public void start() throws IllegalArgumentException, SecurityException, InstantiationException,
IllegalAccessException, InvocationTargetException, NoSuchMethodException,
ComponentInitException {
+ dropAndCreateTable();
+
long overallStartTime = System.currentTimeMillis();
// ComponentManager cm = ComponentManager.getInstance();
@@ -385,7 +404,7 @@
learnedAxioms.add(new EvaluatedAxiom(null, new AxiomScore(learnedDescription.getAccuracy())));
}
}
- if(timeout){
+ if(timeout && learnedAxioms.isEmpty()){
writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
} else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "NULL", 0, runTime, false);
@@ -419,26 +438,82 @@
public void printResultsLaTeX() throws Exception{
double threshold = 0.9;
- for(Class<? extends LearningAlgorithm> algo : classAlgorithms){
+ List<Class<? extends LearningAlgorithm>> algorithms = new ArrayList<Class<? extends LearningAlgorithm>>();
+ algorithms.addAll(classAlgorithms);
+ algorithms.addAll(objectPropertyAlgorithms);
+ algorithms.addAll(dataPropertyAlgorithms);
+
+ for(Class<? extends LearningAlgorithm> algo : algorithms){
String algoName = algo.getAnnotation(ComponentAnn.class).name();
+
+ //get number of entities
+ ps = conn.prepareStatement("SELECT COUNT(DISTINCT entity) FROM evaluation WHERE algorithm=?");
+ ps.setString(1, algoName);
+ java.sql.ResultSet rs = ps.executeQuery();
+ rs.next();
+ int overallNumberOfEntities = rs.getInt(1);
+
+ //get number of entities with empty result
+ ps = conn.prepareStatement("SELECT COUNT(DISTINCT entity) FROM evaluation WHERE algorithm=? AND axiom=?");
+ ps.setString(1, algoName);
+ ps.setString(2, "NULL");
+ rs = ps.executeQuery();
+ rs.next();
+ int numberOfEntitiesWithEmptyResult = rs.getInt(1);
+
+ //get number of entities with timout
+ ps = conn.prepareStatement("SELECT COUNT(DISTINCT entity) FROM evaluation WHERE algorithm=? AND axiom=?");
+ ps.setString(1, algoName);
+ ps.setString(2, "TIMEOUT");
+ rs = ps.executeQuery();
+ rs.next();
+ 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.setString(1, algoName);
ps.setDouble(2, threshold);
- java.sql.ResultSet rs = ps.executeQuery();
- rs.next();System.out.println(rs.getDouble(1));
- int avgSuggestionsAboveThreshold = rs.getInt(1);
+ rs = ps.executeQuery();
+ rs.next();
+ double avgSuggestionsAboveThreshold = rs.getDouble(1);
//compute average runtime
- ps = conn.prepareStatement("SELECT AVG(runtime_ms) FROM evaluation WHERE algorithm=?");
+ ps = conn.prepareStatement("SELECT AVG(runtime) FROM (SELECT MAX(runtime_ms) AS runtime FROM evaluation WHERE algorithm=?) AS A");
ps.setString(1, algoName);
rs = ps.executeQuery();
rs.next();
- int avgRuntimeInMilliseconds = rs.getInt(1);
+ double avgRuntimeInMilliseconds = rs.getDouble(1);
- //compute
+ //compute ratio for complete timeouts
+ double timeoutRatio = (double)numberOfEntitiesWithTimeout / overallNumberOfEntities;
- System.out.println(algoName + "-" + avgSuggestionsAboveThreshold + "-" + avgRuntimeInMilliseconds);
+ //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.setString(1, algoName);
+ ps.setDouble(2, threshold);
+ rs = ps.executeQuery();
+ rs.next();
+ double avgScore= 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.setString(1, algoName);
+ ps.setDouble(2, threshold);
+ rs = ps.executeQuery();
+ rs.next();
+ double avgMaxScore = 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. runtime in ms: " + avgRuntimeInMilliseconds);
+ System.out.println("Timeout ratio: " + timeoutRatio);
+ System.out.println("Avg. score: " + avgScore);
+ System.out.println("Avg. max. score: " + avgMaxScore);
+
}
}
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
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());
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <lor...@us...> - 2011-08-25 15:09:20
|
Revision: 3124
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3124&view=rev
Author: lorenz_b
Date: 2011-08-25 15:09:12 +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 12:17:57 UTC (rev 3123)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-25 15:09:12 UTC (rev 3124)
@@ -44,6 +44,8 @@
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
+import java.util.SortedSet;
+import java.util.TreeSet;
import java.util.Map.Entry;
import java.util.Set;
import java.util.prefs.Preferences;
@@ -76,25 +78,42 @@
import org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner;
import org.dllearner.core.AbstractCELA;
+import org.dllearner.core.AbstractReasonerComponent;
import org.dllearner.core.AnnComponentManager;
import org.dllearner.core.AxiomLearningAlgorithm;
import org.dllearner.core.ComponentAnn;
import org.dllearner.core.ComponentInitException;
+import org.dllearner.core.ComponentManager;
import org.dllearner.core.EvaluatedAxiom;
import org.dllearner.core.EvaluatedDescription;
import org.dllearner.core.LearningAlgorithm;
+import org.dllearner.core.LearningProblemUnsupportedException;
+import org.dllearner.core.Score;
import org.dllearner.core.config.ConfigHelper;
+import org.dllearner.core.configurators.CELOEConfigurator;
import org.dllearner.core.owl.Axiom;
import org.dllearner.core.owl.DatatypeProperty;
import org.dllearner.core.owl.DisjointClassesAxiom;
+import org.dllearner.core.owl.EquivalentClassesAxiom;
+import org.dllearner.core.owl.Individual;
import org.dllearner.core.owl.NamedClass;
import org.dllearner.core.owl.ObjectProperty;
+import org.dllearner.core.owl.SubClassAxiom;
import org.dllearner.kb.SparqlEndpointKS;
import org.dllearner.kb.sparql.SPARQLTasks;
import org.dllearner.kb.sparql.SparqlEndpoint;
+import org.dllearner.kb.sparql.SparqlKnowledgeSource;
import org.dllearner.learningproblems.AxiomScore;
+import org.dllearner.learningproblems.ClassLearningProblem;
+import org.dllearner.reasoning.FastInstanceChecker;
+import org.dllearner.reasoning.SPARQLReasoner;
import org.dllearner.utilities.CommonPrefixMap;
import org.dllearner.utilities.Files;
+import org.dllearner.utilities.Helper;
+import org.dllearner.utilities.datastructures.Datastructures;
+import org.dllearner.utilities.datastructures.SetManipulation;
+import org.dllearner.utilities.datastructures.SortedSetTuple;
+import org.dllearner.utilities.examples.AutomaticNegativeExampleFinderSPARQL2;
import org.dllearner.utilities.owl.DLLearnerAxiomConvertVisitor;
import org.dllearner.utilities.owl.OWLAPIConverter;
import org.ini4j.IniPreferences;
@@ -149,9 +168,9 @@
private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();//getEndpointDBpediaLiveAKSW();
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 3;
- private int maxDataProperties = 3;
- private int maxClasses = 3;
+ private int maxObjectProperties = 10;
+ private int maxDataProperties = 10;
+ private int maxClasses = 10;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -288,12 +307,12 @@
}
- private void evaluateObjectProperties(SparqlEndpointKS ks)throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ private void evaluateObjectProperties(SparqlEndpointKS ks)throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<ObjectProperty> properties = new SPARQLTasks(ks.getEndpoint()).getAllObjectProperties();
for (Class<? extends AxiomLearningAlgorithm> algorithmClass : objectPropertyAlgorithms) {
int objectProperties = 0;
-
+ Thread.sleep(10000);
String algName = "";
for (ObjectProperty property : properties) {
@@ -326,8 +345,9 @@
try {
learner.start();
} catch (Exception e) {
+ timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
} else {
e.printStackTrace();
}
@@ -364,12 +384,12 @@
}
}
- private void evaluateDataProperties(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ private void evaluateDataProperties(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<DatatypeProperty> properties = new SPARQLTasks(ks.getEndpoint()).getAllDataProperties();
for (Class<? extends AxiomLearningAlgorithm> algorithmClass : dataPropertyAlgorithms) {
int dataProperties = 0;
-
+ Thread.sleep(10000);
String algName = "";
for (DatatypeProperty property : properties) {
@@ -402,8 +422,9 @@
try {
learner.start();
} catch (Exception e) {
+ timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
} else {
e.printStackTrace();
}
@@ -439,11 +460,12 @@
}
}
- private void evaluateClasses(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException{
+ private void evaluateClasses(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<NamedClass> classes = new SPARQLTasks(ks.getEndpoint()).getAllClasses();
for (Class<? extends LearningAlgorithm> algorithmClass : classAlgorithms) {
int classesCnt = 0;
+ Thread.sleep(10000);
for (NamedClass cls : classes) {
try{
@@ -475,8 +497,9 @@
try {
learner.start();
} catch (Exception e) {
+ timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
- timeout = true;
+
} else {
e.printStackTrace();
}
@@ -489,12 +512,7 @@
learnedAxioms = ((AxiomLearningAlgorithm)learner)
.getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
} else if(learner instanceof AbstractCELA){
- learnedAxioms = new ArrayList<EvaluatedAxiom>();
- List<? extends EvaluatedDescription> learnedDescriptions = ((AbstractCELA)learner).getCurrentlyBestEvaluatedDescriptions(nrOfAxiomsToLearn);
- for(EvaluatedDescription learnedDescription : learnedDescriptions){
- //TODO create axioms and differ between equivalent and subclass mode in CELOE
- learnedAxioms.add(new EvaluatedAxiom(null, new AxiomScore(learnedDescription.getAccuracy())));
- }
+ learnedAxioms = applyCELOE(ks, cls, false);
}
if(timeout && learnedAxioms.isEmpty()){
writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
@@ -522,7 +540,88 @@
}
}
}
+
+ private List<EvaluatedAxiom> applyCELOE(SparqlEndpointKS ks, NamedClass nc, boolean equivalence) throws ComponentInitException {
+ // get instances of class as positive examples
+ SPARQLReasoner sr = new SPARQLReasoner(ks);
+ SortedSet<Individual> posExamples = sr.getIndividuals(nc, 20);
+ SortedSet<String> posExStr = Helper.getStringSet(posExamples);
+
+ // get negative examples via various strategies
+ System.out.print("finding negatives ... ");
+ AutomaticNegativeExampleFinderSPARQL2 finder = new AutomaticNegativeExampleFinderSPARQL2(ks.getEndpoint());
+ SortedSet<String> negExStr = finder.getNegativeExamples(nc.getName(), posExStr);
+ negExStr = SetManipulation.fuzzyShrink(negExStr, 20);
+ SortedSet<Individual> negExamples = Helper.getIndividualSet(negExStr);
+ SortedSetTuple<Individual> examples = new SortedSetTuple<Individual>(posExamples, negExamples);
+
+ System.out.println("done (" + negExStr.size()+ ")");
+
+ ComponentManager cm = ComponentManager.getInstance();
+ SparqlKnowledgeSource ks2 = cm.knowledgeSource(SparqlKnowledgeSource.class);
+ ks2.getConfigurator().setInstances(Datastructures.individualSetToStringSet(examples.getCompleteSet()));
+ ks2.getConfigurator().setUrl(ks.getEndpoint().getURL());
+ ks2.getConfigurator().setDefaultGraphURIs(new TreeSet<String>(ks.getEndpoint().getDefaultGraphURIs()));
+ ks2.getConfigurator().setUseLits(false);
+ ks2.getConfigurator().setUseCacheDatabase(true);
+ ks2.getConfigurator().setRecursionDepth(2);
+ ks2.getConfigurator().setCloseAfterRecursion(true);
+// ks2.getConfigurator().setSaveExtractedFragment(true);
+ System.out.println("getting fragment ... ");
+ ks2.init();
+ System.out.println("done");
+
+ AbstractReasonerComponent rc = cm.reasoner(FastInstanceChecker.class, ks2);
+ rc.init();
+
+ // TODO: super class learning
+ ClassLearningProblem lp = cm.learningProblem(ClassLearningProblem.class, rc);
+// lp.setPositiveExamples(posExamples);
+// lp.setNegativeExamples(negExamples);
+ try {
+ lp.getConfigurator().setClassToDescribe(nc.getURI().toURL());
+ } catch (MalformedURLException e1) {
+ e1.printStackTrace();
+ }
+ lp.getConfigurator().setType("equivalence");
+ lp.getConfigurator().setAccuracyMethod("fmeasure");
+ lp.getConfigurator().setUseApproximations(false);
+ lp.getConfigurator().setMaxExecutionTimeInSeconds(10);
+ lp.init();
+
+ CELOE la = null;
+ try {
+ la = cm.learningAlgorithm(CELOE.class, lp, rc);
+ } catch (LearningProblemUnsupportedException e) {
+ e.printStackTrace();
+ }
+ CELOEConfigurator cc = la.getConfigurator();
+ cc.setMaxExecutionTimeInSeconds(10);
+ cc.setNoisePercentage(25);
+ la.init();
+ System.out.print("running CELOE ... ");
+ la.start();
+ System.out.println("done");
+
+ // convert the result to axioms (to make it compatible with the other algorithms)
+ List<? extends EvaluatedDescription> learnedDescriptions = la.getCurrentlyBestEvaluatedDescriptions(threshold);
+ List<EvaluatedAxiom> evaluatedAxioms = new LinkedList<EvaluatedAxiom>();
+ for(EvaluatedDescription learnedDescription : learnedDescriptions) {
+ Axiom axiom;
+ if(equivalence) {
+ axiom = new EquivalentClassesAxiom(nc, learnedDescription.getDescription());
+ } else {
+ axiom = new SubClassAxiom(nc, learnedDescription.getDescription());
+ }
+ Score score = lp.computeScore(learnedDescription.getDescription());
+ evaluatedAxioms.add(new EvaluatedAxiom(axiom, score));
+ }
+
+ cm.freeAllComponents();
+ return evaluatedAxioms;
+ }
+
public void printResultsPlain() {
}
@@ -540,7 +639,7 @@
ps.execute();
StringBuilder table1 = new StringBuilder();
- table1.append("\\begin{tabulary}{\\textwidth}{LCCCCC}\\toprule\n");
+ table1.append("\\begin{tabulary}{\\textwidth}{LRRRRR}\\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){
@@ -615,7 +714,7 @@
System.out.println("Avg. max. score: " + avgMaxScore);
table1.
- append(algoName.replace("learner", "").trim()).append(" & ").
+ append(algoName.replace("axiom learner", "").trim()).append(" & ").
append(avgSuggestionsAboveThreshold).append(" & ").
append(avgRuntimeInMilliseconds).append(" & ").
append(timeoutRatio).append(" & ").
@@ -624,33 +723,19 @@
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}));
+ //second part of evaluation
- 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}));
+ StringBuilder table2 = new StringBuilder();
+ table2.append("\\begin{tabulary}{\\textwidth}{LCCCCC}\\toprule\n");
+ table2.append("& & & \\multicolumn{3}{c}{Estimated precision} \\\\\n");
+ table2.append(" axiom type & recall & additional axioms & no & maybe & yes \\\\\\midrule\n");
- 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 axiomtypes and corresponding algorithm
+ Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> axiomType2Algorithm = getAxiomTypesWithLearningAlgorithms();
// 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
@@ -668,7 +753,7 @@
}
}
- //compute for each axiom type the recall
+ //compute recall for each axiom type
for(Entry<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> entry : axiomType2Algorithm.entrySet()){
AxiomType<? extends OWLAxiom> type = entry.getKey();
algorithms = entry.getValue();
@@ -685,10 +770,41 @@
}
}
}
+ table2.
+ append(type.getName()).append(" & ").
+ append(found + "/" + all ).append(" & & & & \\\\\n");
System.out.println(type.getName() + ": " + found + "/" + all);
}
+
+ table2.append("\\end{tabulary}");
+ System.out.println(table2.toString());
}
+ private Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> getAxiomTypesWithLearningAlgorithms(){
+ 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}));
+ return axiomType2Algorithm;
+ }
+
private boolean isRelevantAxiom(OWLAxiom axiom, Set<OWLEntity> entities){
if(axiom instanceof OWLObjectPropertyAxiom){
return containsOneOf(axiom.getObjectPropertiesInSignature(), entities);
@@ -731,7 +847,7 @@
}
private double round(double value){
- return Math.round( value * 100. ) / 100.;
+ return Math.round( value * 10. ) / 10.;
}
public String printHTMLTable() throws SQLException {
@@ -793,9 +909,11 @@
}
}
- public static void main(String[] args) throws Exception {
+ 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());
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <lor...@us...> - 2011-08-27 14:39:54
|
Revision: 3139
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3139&view=rev
Author: lorenz_b
Date: 2011-08-27 14:39:48 +0000 (Sat, 27 Aug 2011)
Log Message:
-----------
Added additional axiom column to table.
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 2011-08-27 12:12:38 UTC (rev 3138)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-27 14:39:48 UTC (rev 3139)
@@ -50,6 +50,7 @@
import java.util.Set;
import java.util.prefs.Preferences;
+import org.apache.commons.collections.SetUtils;
import org.apache.commons.compress.compressors.CompressorException;
import org.apache.commons.compress.compressors.CompressorInputStream;
import org.apache.commons.compress.compressors.CompressorStreamFactory;
@@ -702,17 +703,6 @@
rs.next();
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. 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("axiom learner", "").trim()).append(" & ").
append(avgSuggestionsAboveThreshold).append(" & ").
@@ -753,27 +743,40 @@
}
}
+
+
//compute recall for each axiom type
+ ps = conn.prepareStatement("SELECT axiom FROM evaluation WHERE algorithm=? AND score>=?");
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);
- }
- }
+ ps.setString(1, algorithms.get(0).getAnnotation(ComponentAnn.class).name());
+ ps.setDouble(2, threshold);
+
+ //get all found axioms for specific axiom type
+ Set<String> foundAxioms = new TreeSet<String>();
+ rs = ps.executeQuery();
+ while(rs.next()){
+ foundAxioms.add(rs.getString(1));
}
+
+
+ Set<String> relevantAxioms = getRelevantAxioms(type, entities);
+
+ Set<String> notFoundAxioms = org.mindswap.pellet.utils.SetUtils.difference(relevantAxioms, foundAxioms);
+
+ Set<String> additionalAxioms = org.mindswap.pellet.utils.SetUtils.difference(foundAxioms, relevantAxioms);
+
+ int total = relevantAxioms.size();
+ int found = total-notFoundAxioms.size();
+
table2.
append(type.getName()).append(" & ").
- append(found + "/" + all ).append(" & & & & \\\\\n");
- System.out.println(type.getName() + ": " + found + "/" + all);
+ append( found + "/" + total ).append(" & ").
+ append(additionalAxioms.size()).
+ append(" & & & \\\\\n");
+ System.out.println(type.getName() + ": " + found + "/" + total);
}
table2.append("\\end{tabulary}");
@@ -805,6 +808,19 @@
return axiomType2Algorithm;
}
+ private Set<String> getRelevantAxioms(AxiomType<? extends OWLAxiom> axiomType, Set<OWLEntity> entities){
+ Set<String> relevantAxioms = new HashSet<String>();
+ for(OWLAxiom axiom : dbPediaOntology.getAxioms(axiomType)){
+ if(!axiom.getClassesInSignature().contains(factory.getOWLThing())){
+ if(isRelevantAxiom(axiom, entities)){
+ String axiomString = DLLearnerAxiomConvertVisitor.getDLLearnerAxiom(axiom).toManchesterSyntaxString(baseURI, prefixes);
+ relevantAxioms.add(axiomString);
+ }
+ }
+ }
+ return relevantAxioms;
+ }
+
private boolean isRelevantAxiom(OWLAxiom axiom, Set<OWLEntity> entities){
if(axiom instanceof OWLObjectPropertyAxiom){
return containsOneOf(axiom.getObjectPropertiesInSignature(), entities);
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|
|
From: <lor...@us...> - 2011-08-29 09:11:44
|
Revision: 3157
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3157&view=rev
Author: lorenz_b
Date: 2011-08-29 09:11:38 +0000 (Mon, 29 Aug 2011)
Log Message:
-----------
Added method to get latest DBpedia ontology from SPARQL endpoint.
Writing missed axioms into folder evaluation/missed/ and additional axioms into evaluation/additional/ .
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 2011-08-29 08:52:56 UTC (rev 3156)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-29 09:11:38 UTC (rev 3157)
@@ -21,6 +21,8 @@
import java.io.BufferedInputStream;
import java.io.BufferedWriter;
+import java.io.ByteArrayInputStream;
+import java.io.ByteArrayOutputStream;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
@@ -50,6 +52,7 @@
import java.util.Map.Entry;
import java.util.Set;
import java.util.SortedSet;
+import java.util.TreeMap;
import java.util.TreeSet;
import java.util.prefs.Preferences;
@@ -131,12 +134,20 @@
import org.semanticweb.owlapi.model.OWLOntology;
import org.semanticweb.owlapi.model.OWLOntologyCreationException;
import org.semanticweb.owlapi.model.OWLSubClassOfAxiom;
+import org.semanticweb.owlapi.reasoner.InconsistentOntologyException;
import org.semanticweb.owlapi.reasoner.InferenceType;
import org.semanticweb.owlapi.reasoner.OWLReasoner;
+import org.semanticweb.owlapi.reasoner.ReasonerInterruptedException;
+import org.semanticweb.owlapi.reasoner.TimeOutException;
import uk.ac.manchester.cs.owl.owlapi.OWLDataFactoryImpl;
import com.clarkparsia.pellet.owlapiv3.PelletReasonerFactory;
+import com.hp.hpl.jena.query.QueryExecution;
+import com.hp.hpl.jena.query.QueryExecutionFactory;
+import com.hp.hpl.jena.query.QueryFactory;
+import com.hp.hpl.jena.rdf.model.Model;
+import com.hp.hpl.jena.rdf.model.ModelFactory;
/**
* Evaluation of enrichment algorithms on DBpedia (Live).
@@ -171,7 +182,7 @@
// can be used to only evaluate a part of DBpedia
private int maxObjectProperties = 10;
private int maxDataProperties = 10;
- private int maxClasses = 10;
+ private int maxClasses = 3;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -295,12 +306,12 @@
SparqlEndpointKS ks = new SparqlEndpointKS(endpoint);
ks.init();
- evaluateObjectProperties(ks);
+// evaluateObjectProperties(ks);
//
// Thread.sleep(10000);
-
- evaluateDataProperties(ks);
-
+//
+// evaluateDataProperties(ks);
+//
// Thread.sleep(10000);
//
evaluateClasses(ks);
@@ -748,7 +759,7 @@
//compute recall for each axiom type
- ps = conn.prepareStatement("SELECT axiom FROM evaluation WHERE algorithm=? AND score>=?");
+ ps = conn.prepareStatement("SELECT axiom, entailed, score FROM evaluation WHERE algorithm=? AND score>=?");
for(Entry<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> entry : axiomType2Algorithm.entrySet()){
AxiomType<? extends OWLAxiom> type = entry.getKey();
algorithms = entry.getValue();
@@ -758,21 +769,35 @@
//get all found axioms for specific axiom type
Set<String> foundAxioms = new TreeSet<String>();
+ Map<String, Double> foundAndNotEntailedAxioms = new TreeMap<String, Double>();
rs = ps.executeQuery();
+ String axiom;
+ boolean entailed;
+ double score;
while(rs.next()){
- foundAxioms.add(rs.getString(1));
+ axiom = rs.getString(1);
+ entailed = rs.getBoolean(2);
+ score = rs.getDouble(3);
+
+ foundAxioms.add(axiom);
+ if(!entailed){
+ foundAndNotEntailedAxioms.put(axiom, score);
+ }
}
-
+ //get all axioms in the reference ontology for a specific axiom type
Set<String> relevantAxioms = getRelevantAxioms(type, entities);
+ //compute the axioms which are in the reference ontology, but not be computed by the learning algorithm
+ Set<String> missedAxioms = org.mindswap.pellet.utils.SetUtils.difference(relevantAxioms, foundAxioms);
+ System.out.println(missedAxioms);
+ //compute the additional found axioms which were not entailed
+ for(String relAxiom : relevantAxioms){
+ foundAndNotEntailedAxioms.remove(relAxiom);
+ }
+ Set<String> additionalAxioms = foundAndNotEntailedAxioms.keySet();
- Set<String> notFoundAxioms = org.mindswap.pellet.utils.SetUtils.difference(relevantAxioms, foundAxioms);
- System.out.println(notFoundAxioms);
-
- Set<String> additionalAxioms = org.mindswap.pellet.utils.SetUtils.difference(foundAxioms, relevantAxioms);
-
int total = relevantAxioms.size();
- int found = total-notFoundAxioms.size();
+ int found = total - missedAxioms.size();
table2.
append(type.getName()).append(" & ").
@@ -782,22 +807,56 @@
System.out.println(type.getName() + ": " + found + "/" + total);
- //write additional axioms into file
- writeToDisk(type, additionalAxioms);
+ //write additional axioms with score into file
+ writeToDisk(type, foundAndNotEntailedAxioms);
+ //write missed axioms into file
+ writeToDisk(type, missedAxioms);
}
table2.append("\\end{tabulary}");
System.out.println(table2.toString());
}
+ private void writeToDisk(AxiomType<? extends OWLAxiom> axiomType, Map<String, Double> axiomsWithAccurracy){
+ String fileName = axiomType.getName().replaceAll(" ", "_") + ".txt";
+
+ BufferedWriter out = null;
+ try {
+ File dir = new File("evaluation/additional");
+ if(!dir.exists()){
+ dir.mkdirs();
+ }
+
+ File file = new File(dir + File.separator + fileName);
+ if(!file.exists()){
+ file.createNewFile();
+ }
+ out = new BufferedWriter(new FileWriter(file));
+ for(Entry<String, Double> entry : axiomsWithAccurracy.entrySet()){
+ out.write(entry.getKey() + " (" + round(entry.getValue())*100 + "%)");
+ out.newLine();
+ }
+ } catch (IOException e) {
+ e.printStackTrace();
+ } finally {
+ if(out != null){
+ try {
+ out.close();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ }
+ }
+ }
+
private void writeToDisk(AxiomType<? extends OWLAxiom> axiomType, Set<String> axioms){
String fileName = axiomType.getName().replaceAll(" ", "_") + ".txt";
BufferedWriter out = null;
try {
- File dir = new File("evaluation");
+ File dir = new File("evaluation/missed");
if(!dir.exists()){
- dir.mkdir();
+ dir.mkdirs();
}
File file = new File(dir + File.separator + fileName);
@@ -963,6 +1022,41 @@
e.printStackTrace();
}
}
+
+ private void loadCurrentDBpediaOntology(){
+ int limit = 1000;
+ int offset = 0;
+ String query = "CONSTRUCT {?s ?p ?o.} WHERE {?s ?p ?o} LIMIT %d OFFSET %d";
+ Model model = ModelFactory.createDefaultModel();
+
+ QueryExecution qExec;
+ Model newModel;
+ boolean repeat = true;
+ while(repeat){
+ repeat = false;
+ qExec = QueryExecutionFactory.sparqlService("http://live.dbpedia.org/sparql", QueryFactory.create(String.format(query, limit, offset)), "http://live.dbpedia.org/ontology");
+ newModel = qExec.execConstruct();
+ model.add(newModel);
+ repeat = newModel.size() > 0;
+ offset += limit;
+ }
+ try {
+ ByteArrayOutputStream baos = new ByteArrayOutputStream();
+ model.write(baos, "RDF/XML");
+ ByteArrayInputStream bs = new ByteArrayInputStream(baos.toByteArray());
+ dbPediaOntology = OWLManager.createOWLOntologyManager().loadOntologyFromOntologyDocument(bs);
+ reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
+ reasoner.precomputeInferences(InferenceType.CLASS_HIERARCHY);
+ } catch (TimeOutException e) {
+ e.printStackTrace();
+ } catch (InconsistentOntologyException e) {
+ e.printStackTrace();
+ } catch (ReasonerInterruptedException e) {
+ e.printStackTrace();
+ } catch (OWLOntologyCreationException e) {
+ e.printStackTrace();
+ }
+ }
public static void main(String[] args) throws Exception
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|
|
From: <lor...@us...> - 2011-08-31 07:35:15
|
Revision: 3176
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3176&view=rev
Author: lorenz_b
Date: 2011-08-31 07:35:09 +0000 (Wed, 31 Aug 2011)
Log Message:
-----------
Small changes in 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 2011-08-31 07:34:25 UTC (rev 3175)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-31 07:35:09 UTC (rev 3176)
@@ -180,9 +180,9 @@
// private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 10;
- private int maxDataProperties = 10;
- private int maxClasses = 3;
+ private int maxObjectProperties = 20;
+ private int maxDataProperties = 0;
+ private int maxClasses = 0;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -226,10 +226,11 @@
dataPropertyAlgorithms.add(SubDataPropertyOfAxiomLearner.class);
classAlgorithms = new LinkedList<Class<? extends LearningAlgorithm>>();
- classAlgorithms.add(DisjointClassesLearner.class);
+// classAlgorithms.add(CELOE.class);
+// classAlgorithms.add(DisjointClassesLearner.class);
classAlgorithms.add(SimpleSubclassLearner.class);
-// classAlgorithms.add(CELOE.class);
+
initDBConnection();
loadDBpediaOntology();
}
@@ -260,7 +261,7 @@
}
}
- private void dropAndCreateTable(){
+ public void dropAndCreateTable(){
try {
Statement s = conn.createStatement();
s.executeUpdate("DROP TABLE IF EXISTS evaluation");
@@ -296,24 +297,21 @@
public void start() throws IllegalArgumentException, SecurityException, InstantiationException,
IllegalAccessException, InvocationTargetException, NoSuchMethodException,
ComponentInitException, InterruptedException {
- dropAndCreateTable();
+// dropAndCreateTable();
long overallStartTime = System.currentTimeMillis();
-// ComponentManager cm = ComponentManager.getInstance();
- // create DBpedia Live knowledge source
-
SparqlEndpointKS ks = new SparqlEndpointKS(endpoint);
ks.init();
// evaluateObjectProperties(ks);
//
-// Thread.sleep(10000);
+// Thread.sleep(20000);
//
// evaluateDataProperties(ks);
//
-// Thread.sleep(10000);
-//
+// Thread.sleep(20000);
+
evaluateClasses(ks);
System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
@@ -483,51 +481,58 @@
for (NamedClass cls : classes) {
try{
- // dynamically invoke constructor with SPARQL knowledge source
- LearningAlgorithm learner = algorithmClass.getConstructor(
- SparqlEndpointKS.class).newInstance(ks);
- ConfigHelper.configure(learner, "classToDescribe", cls.toString());
- ConfigHelper.configure(learner, "maxExecutionTimeInSeconds",
- maxExecutionTimeInSeconds);
- learner.init();
- // learner.setPropertyToDescribe(property);
- // learner.setMaxExecutionTimeInSeconds(10);
- String algName = AnnComponentManager.getName(learner);
-
- int attempt = 0;
- long startTime = 0;
- boolean timeout = true;
- while(timeout && attempt++ < maxAttempts){
- timeout = false;
- if(attempt > 1){
+ List<EvaluatedAxiom> learnedAxioms = null;
+ long startTime = System.currentTimeMillis();
+ boolean timeout = false;
+ String algName;
+ if(algorithmClass == CELOE.class){
+ algName = CELOE.class.getSimpleName();
+ System.out.println("Applying " + algName + " on " + cls + " ... ");
+ learnedAxioms = applyCELOE(ks, cls, false);
+ } else {
+
+ // dynamically invoke constructor with SPARQL knowledge source
+ LearningAlgorithm learner = algorithmClass.getConstructor(
+ SparqlEndpointKS.class).newInstance(ks);
+ ConfigHelper.configure(learner, "classToDescribe", cls.toString());
+ ConfigHelper.configure(learner, "maxExecutionTimeInSeconds",
+ maxExecutionTimeInSeconds);
+ learner.init();
+ // learner.setPropertyToDescribe(property);
+ // learner.setMaxExecutionTimeInSeconds(10);
+ algName = AnnComponentManager.getName(learner);
+ int attempt = 0;
+
+ 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 {
- Thread.sleep(delayInMilliseconds);
- } catch (InterruptedException e) {
- e.printStackTrace();
+ learner.start();
+ } catch (Exception e) {
+ timeout = true;
+ if(e.getCause() instanceof SocketTimeoutException){
+
+ } else {
+ e.printStackTrace();
+ }
}
}
- System.out.println("Applying " + algName + " on " + cls + " ... (Attempt " + attempt + ")");
- startTime = System.currentTimeMillis();
- try {
- learner.start();
- } catch (Exception e) {
- timeout = true;
- if(e.getCause() instanceof SocketTimeoutException){
-
- } else {
- e.printStackTrace();
- }
- }
+ learnedAxioms = ((AxiomLearningAlgorithm)learner)
+ .getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
}
+
long runTime = System.currentTimeMillis() - startTime;
- List<EvaluatedAxiom> learnedAxioms = null;
- if(learner instanceof AxiomLearningAlgorithm){
- learnedAxioms = ((AxiomLearningAlgorithm)learner)
- .getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
- } else if(learner instanceof AbstractCELA){
- learnedAxioms = applyCELOE(ks, cls, false);
- }
+
if(timeout && learnedAxioms.isEmpty()){
writeToDB(cls.toManchesterSyntaxString(baseURI, prefixes), algName, "TIMEOUT", 0, runTime, false);
} else if (learnedAxioms == null || learnedAxioms.isEmpty()) {
@@ -1062,6 +1067,7 @@
{
EnrichmentEvaluation ee = new EnrichmentEvaluation();
+ ee.dropAndCreateTable();
ee.start();
// ee.printResultsPlain();
ee.printResultsLaTeX();
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|
|
From: <lor...@us...> - 2011-08-31 13:52:47
|
Revision: 3189
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3189&view=rev
Author: lorenz_b
Date: 2011-08-31 13:52:41 +0000 (Wed, 31 Aug 2011)
Log Message:
-----------
Some small changes.
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 2011-08-31 13:52:34 UTC (rev 3188)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-08-31 13:52:41 UTC (rev 3189)
@@ -123,6 +123,7 @@
import org.dllearner.utilities.owl.OWLAPIConverter;
import org.ini4j.IniPreferences;
import org.ini4j.InvalidFileFormatException;
+import org.semanticweb.HermiT.Reasoner;
import org.semanticweb.owlapi.apibinding.OWLManager;
import org.semanticweb.owlapi.model.AxiomType;
import org.semanticweb.owlapi.model.IRI;
@@ -181,7 +182,7 @@
// private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 20;
+ private int maxObjectProperties = 0;
private int maxDataProperties = 0;
private int maxClasses = 0;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
@@ -206,7 +207,7 @@
prefixes.put("yago", "http://dbpedia.org/class/");
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
- objectPropertyAlgorithms.add(DisjointObjectPropertyAxiomLearner.class);
+// objectPropertyAlgorithms.add(DisjointObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(EquivalentObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(FunctionalObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(InverseFunctionalObjectPropertyAxiomLearner.class);
@@ -233,7 +234,7 @@
initDBConnection();
- loadDBpediaOntology();
+ loadCurrentDBpediaOntology2();
}
private void initDBConnection() {
@@ -248,6 +249,9 @@
Class.forName("com.mysql.jdbc.Driver");
String url = "jdbc:mysql://" + dbServer + "/" + dbName;
conn = DriverManager.getConnection(url, dbUser, dbPass);
+
+ ps = conn.prepareStatement("INSERT INTO evaluation ("
+ + "entity, algorithm, axiom, score, runtime_ms, entailed ) " + "VALUES(?,?,?,?,?,?)");
} catch (ClassNotFoundException e) {
e.printStackTrace();
@@ -271,8 +275,6 @@
+ "entity VARCHAR(200), algorithm VARCHAR(100), axiom VARCHAR(500), score DOUBLE, runtime_ms INT(20), entailed BOOLEAN)");
s.close();
- ps = conn.prepareStatement("INSERT INTO evaluation ("
- + "entity, algorithm, axiom, score, runtime_ms, entailed ) " + "VALUES(?,?,?,?,?,?)");
} catch (SQLException e) {
e.printStackTrace();
}
@@ -308,7 +310,7 @@
// evaluateObjectProperties(ks);
//
// Thread.sleep(20000);
-//
+
// evaluateDataProperties(ks);
//
// Thread.sleep(20000);
@@ -1006,6 +1008,8 @@
private boolean isEntailed(EvaluatedAxiom evalAxiom){
OWLAxiom axiom = OWLAPIConverter.getOWLAPIAxiom(evalAxiom.getAxiom());
boolean entailed = reasoner.isEntailed(axiom);
+// System.out.println(evalAxiom.getAxiom().toManchesterSyntaxString(baseURI, prefixes));
+// System.out.println(entailed);
return entailed;
}
@@ -1049,21 +1053,41 @@
offset += limit;
}
try {
- ByteArrayOutputStream baos = new ByteArrayOutputStream();
- model.write(baos, "RDF/XML");
- ByteArrayInputStream bs = new ByteArrayInputStream(baos.toByteArray());
- dbPediaOntology = OWLManager.createOWLOntologyManager().loadOntologyFromOntologyDocument(bs);
+ dbPediaOntology = convert(model);
reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
reasoner.precomputeInferences(InferenceType.CLASS_HIERARCHY);
+ System.out.println(reasoner.getSuperClasses(
+ factory.getOWLClass(IRI.create("http://dbpedia.org/ontology/Actor")), false).getFlattened());
} catch (TimeOutException e) {
e.printStackTrace();
} catch (InconsistentOntologyException e) {
e.printStackTrace();
} catch (ReasonerInterruptedException e) {
e.printStackTrace();
+ }
+ }
+
+ private void loadCurrentDBpediaOntology2(){
+ System.out.println("Loading schema ...");
+ SPARQLReasoner r = new SPARQLReasoner(new SparqlEndpointKS(endpoint));
+ dbPediaOntology = convert(r.loadSchema());
+ System.out.println("Preparing reasoner ...");
+ reasoner = new Reasoner(dbPediaOntology);
+// reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
+ reasoner.precomputeInferences(InferenceType.CLASS_HIERARCHY);
+ }
+
+ private OWLOntology convert(Model model){
+ OWLOntology ontology = null;
+ try {
+ ByteArrayOutputStream baos = new ByteArrayOutputStream();
+ model.write(baos, "RDF/XML");
+ ByteArrayInputStream bs = new ByteArrayInputStream(baos.toByteArray());
+ ontology = OWLManager.createOWLOntologyManager().loadOntologyFromOntologyDocument(bs);
} catch (OWLOntologyCreationException e) {
e.printStackTrace();
}
+ return ontology;
}
public static void main(String[] args) throws Exception
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|
|
From: <lor...@us...> - 2011-09-01 06:46:27
|
Revision: 3204
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3204&view=rev
Author: lorenz_b
Date: 2011-09-01 06:46:21 +0000 (Thu, 01 Sep 2011)
Log Message:
-----------
Change: Write only the best 100 axioms for each type into file.
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 2011-09-01 04:22:12 UTC (rev 3203)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2011-09-01 06:46:21 UTC (rev 3204)
@@ -43,6 +43,8 @@
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
+import java.util.Collections;
+import java.util.Comparator;
import java.util.HashMap;
import java.util.HashSet;
import java.util.LinkedHashMap;
@@ -217,7 +219,7 @@
objectPropertyAlgorithms.add(ReflexiveObjectPropertyAxiomLearner.class);
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
- dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
+// dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
@@ -486,7 +488,7 @@
boolean timeout = false;
String algName;
if(algorithmClass == CELOE.class){
- algName = CELOE.class.getSimpleName();
+ algName = CELOE.class.getAnnotation(ComponentAnn.class).name();
System.out.println("Applying " + algName + " on " + cls + " ... ");
learnedAxioms = applyCELOE(ks, cls, false);
} else {
@@ -839,9 +841,16 @@
file.createNewFile();
}
out = new BufferedWriter(new FileWriter(file));
- for(Entry<String, Double> entry : axiomsWithAccurracy.entrySet()){
+
+ //sort by values and write only the first 100
+ int i = 0;
+ for(Entry<String, Double> entry : sortByValues(axiomsWithAccurracy)){
+ i++;
out.write(entry.getKey() + " (" + round(entry.getValue())*100 + "%)");
out.newLine();
+ if(i == 100){
+ break;
+ }
}
} catch (IOException e) {
e.printStackTrace();
@@ -888,6 +897,18 @@
}
}
+ protected <K, V extends Comparable<V>> List<Entry<K, V>> sortByValues(Map<K, V> map){
+ List<Entry<K, V>> entries = new ArrayList<Entry<K, V>>(map.entrySet());
+ Collections.sort(entries, new Comparator<Entry<K, V>>() {
+
+ @Override
+ public int compare(Entry<K, V> o1, Entry<K, V> o2) {
+ return o2.getValue().compareTo(o1.getValue());
+ }
+ });
+ return entries;
+ }
+
private Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> getAxiomTypesWithLearningAlgorithms(){
Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> axiomType2Algorithm = new LinkedHashMap<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>>();
axiomType2Algorithm.put(AxiomType.SUBCLASS_OF, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{SimpleSubclassLearner.class, CELOE.class}));
@@ -899,7 +920,7 @@
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.FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalObjectPropertyAxiomLearner.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}));
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|
|
From: <lor...@us...> - 2012-04-30 10:17:52
|
Revision: 3670
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3670&view=rev
Author: lorenz_b
Date: 2012-04-30 10:17:46 +0000 (Mon, 30 Apr 2012)
Log Message:
-----------
Modified 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 2012-04-30 09:04:11 UTC (rev 3669)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2012-04-30 10:17:46 UTC (rev 3670)
@@ -84,6 +84,7 @@
import org.dllearner.algorithms.properties.SubObjectPropertyOfAxiomLearner;
import org.dllearner.algorithms.properties.SymmetricObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.TransitiveObjectPropertyAxiomLearner;
+import org.dllearner.core.AbstractAxiomLearningAlgorithm;
import org.dllearner.core.AbstractReasonerComponent;
import org.dllearner.core.AnnComponentManager;
import org.dllearner.core.AxiomLearningAlgorithm;
@@ -306,15 +307,15 @@
SparqlEndpointKS ks = new SparqlEndpointKS(endpoint);
ks.init();
-// evaluateObjectProperties(ks);
-//
-// Thread.sleep(20000);
+ evaluateObjectProperties(ks);
+ Thread.sleep(20000);
+
// evaluateDataProperties(ks);
//
// Thread.sleep(20000);
- evaluateClasses(ks);
+// evaluateClasses(ks);
System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
@@ -344,8 +345,7 @@
int attempt = 0;
long startTime = 0;
boolean timeout = true;
- while(timeout && attempt++ < maxAttempts){
- timeout = false;
+ while(((AbstractAxiomLearningAlgorithm)learner).isTimeout() && attempt++ < maxAttempts){
if(attempt > 1){
try {
System.out.println("Got timeout. Waiting " + delayInMilliseconds + " ms ...");
@@ -358,8 +358,8 @@
startTime = System.currentTimeMillis();
try {
learner.start();
+ timeout = false;
} catch (Exception e) {
- timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
} else {
@@ -422,8 +422,7 @@
int attempt = 0;
long startTime = 0;
boolean timeout = true;
- while(timeout && attempt++ < maxAttempts){
- timeout = false;
+ while(((AbstractAxiomLearningAlgorithm)learner).isTimeout() && attempt++ < maxAttempts){
if(attempt > 1){
try {
Thread.sleep(delayInMilliseconds);
@@ -435,8 +434,8 @@
startTime = System.currentTimeMillis();
try {
learner.start();
+ timeout = false;
} catch (Exception e) {
- timeout = true;
if(e.getCause() instanceof SocketTimeoutException){
} else {
@@ -506,8 +505,7 @@
int attempt = 0;
timeout = true;
- while(timeout && attempt++ < maxAttempts){
- timeout = false;
+ while(((AbstractAxiomLearningAlgorithm)learner).isTimeout() && attempt++ < maxAttempts){
if(attempt > 1){
try {
Thread.sleep(delayInMilliseconds);
@@ -519,17 +517,12 @@
startTime = System.currentTimeMillis();
try {
learner.start();
+ timeout = false;
} catch (Exception e) {
- timeout = true;
- if(e.getCause() instanceof SocketTimeoutException){
-
- } else {
- e.printStackTrace();
- }
+ e.printStackTrace();
}
}
- learnedAxioms = ((AxiomLearningAlgorithm)learner)
- .getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
+ learnedAxioms = ((AxiomLearningAlgorithm)learner).getCurrentlyBestEvaluatedAxioms(nrOfAxiomsToLearn);
}
@@ -1090,8 +1083,8 @@
SPARQLReasoner r = new SPARQLReasoner(new SparqlEndpointKS(endpoint));
dbPediaOntology = convert(r.loadSchema());
System.out.println("Preparing reasoner ...");
- reasoner = new Reasoner(dbPediaOntology);
-// reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
+// reasoner = new Reasoner(dbPediaOntology);
+ reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
reasoner.precomputeInferences(InferenceType.CLASS_HIERARCHY);
}
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|
|
From: <lor...@us...> - 2012-04-30 11:59:37
|
Revision: 3672
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3672&view=rev
Author: lorenz_b
Date: 2012-04-30 11:59:31 +0000 (Mon, 30 Apr 2012)
Log Message:
-----------
Modified 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 2012-04-30 10:19:21 UTC (rev 3671)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2012-04-30 11:59:31 UTC (rev 3672)
@@ -24,7 +24,9 @@
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.File;
+import java.io.FileInputStream;
import java.io.FileNotFoundException;
+import java.io.FileOutputStream;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
@@ -125,6 +127,7 @@
import org.ini4j.InvalidFileFormatException;
import org.semanticweb.HermiT.Reasoner;
import org.semanticweb.owlapi.apibinding.OWLManager;
+import org.semanticweb.owlapi.io.RDFXMLOntologyFormat;
import org.semanticweb.owlapi.model.AxiomType;
import org.semanticweb.owlapi.model.IRI;
import org.semanticweb.owlapi.model.OWLAxiom;
@@ -135,6 +138,7 @@
import org.semanticweb.owlapi.model.OWLObjectPropertyAxiom;
import org.semanticweb.owlapi.model.OWLOntology;
import org.semanticweb.owlapi.model.OWLOntologyCreationException;
+import org.semanticweb.owlapi.model.OWLOntologyStorageException;
import org.semanticweb.owlapi.model.OWLSubClassOfAxiom;
import org.semanticweb.owlapi.reasoner.InconsistentOntologyException;
import org.semanticweb.owlapi.reasoner.InferenceType;
@@ -178,7 +182,7 @@
// only axioms with a score above this threshold will be considered
private double threshold = 0.7;
- private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpediaLiveAKSW();
+ private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();
// private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();
// can be used to only evaluate a part of DBpedia
@@ -229,7 +233,7 @@
classAlgorithms = new LinkedList<Class<? extends LearningAlgorithm>>();
// classAlgorithms.add(CELOE.class);
-// classAlgorithms.add(DisjointClassesLearner.class);
+ classAlgorithms.add(DisjointClassesLearner.class);
classAlgorithms.add(SimpleSubclassLearner.class);
@@ -307,7 +311,7 @@
SparqlEndpointKS ks = new SparqlEndpointKS(endpoint);
ks.init();
- evaluateObjectProperties(ks);
+// evaluateObjectProperties(ks);
Thread.sleep(20000);
@@ -315,7 +319,7 @@
//
// Thread.sleep(20000);
-// evaluateClasses(ks);
+ evaluateClasses(ks);
System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
@@ -1079,9 +1083,30 @@
}
private void loadCurrentDBpediaOntology2(){
- System.out.println("Loading schema ...");
- SPARQLReasoner r = new SPARQLReasoner(new SparqlEndpointKS(endpoint));
- dbPediaOntology = convert(r.loadSchema());
+ dbPediaOntology = null;
+ try {
+ dbPediaOntology = OWLManager.createOWLOntologyManager().loadOntologyFromOntologyDocument(new FileInputStream(new File("evaluation/currentDBpediaSchema.owl")));
+ } catch (OWLOntologyCreationException e1) {
+ e1.printStackTrace();
+ } catch (FileNotFoundException e1) {
+ e1.printStackTrace();
+ }
+ if(dbPediaOntology == null){
+ System.out.println("Loading schema ...");
+ SPARQLReasoner r = new SPARQLReasoner(new SparqlEndpointKS(endpoint));
+ dbPediaOntology = convert(r.loadSchema());
+ try {
+ new File("evaluation").mkdir();
+ new File("evaluation/currentDBpediaSchema.owl").createNewFile();
+ OWLManager.createOWLOntologyManager().saveOntology(dbPediaOntology, new RDFXMLOntologyFormat(), new FileOutputStream(new File("evaluation/currentDBpediaSchema.owl")));
+ } catch (OWLOntologyStorageException e) {
+ e.printStackTrace();
+ } catch (FileNotFoundException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ }
System.out.println("Preparing reasoner ...");
// reasoner = new Reasoner(dbPediaOntology);
reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <lor...@us...> - 2012-05-02 09:27:00
|
Revision: 3674
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3674&view=rev
Author: lorenz_b
Date: 2012-05-02 09:26:50 +0000 (Wed, 02 May 2012)
Log Message:
-----------
Made script more flexible from command line and added algorithms.
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 2012-05-01 07:53:23 UTC (rev 3673)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2012-05-02 09:26:50 UTC (rev 3674)
@@ -19,7 +19,10 @@
*/
package org.dllearner.scripts.evaluation;
+import static java.util.Arrays.asList;
+
import java.io.BufferedInputStream;
+import java.io.BufferedOutputStream;
import java.io.BufferedWriter;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
@@ -31,9 +34,11 @@
import java.io.FileWriter;
import java.io.IOException;
import java.io.InputStream;
+import java.io.StringWriter;
import java.lang.reflect.InvocationTargetException;
import java.net.MalformedURLException;
import java.net.SocketTimeoutException;
+import java.net.URI;
import java.net.URL;
import java.sql.Connection;
import java.sql.DriverManager;
@@ -60,6 +65,10 @@
import java.util.TreeSet;
import java.util.prefs.Preferences;
+import joptsimple.OptionException;
+import joptsimple.OptionParser;
+import joptsimple.OptionSet;
+
import org.apache.commons.compress.compressors.CompressorException;
import org.apache.commons.compress.compressors.CompressorInputStream;
import org.apache.commons.compress.compressors.CompressorStreamFactory;
@@ -70,14 +79,17 @@
import org.dllearner.algorithms.DisjointClassesLearner;
import org.dllearner.algorithms.SimpleSubclassLearner;
import org.dllearner.algorithms.celoe.CELOE;
+import org.dllearner.algorithms.properties.AsymmetricObjectPropertyAxiomLearner;
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.InverseObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.IrreflexiveObjectPropertyAxiomLearner;
import org.dllearner.algorithms.properties.ObjectPropertyDomainAxiomLearner;
import org.dllearner.algorithms.properties.ObjectPropertyRangeAxiomLearner;
@@ -107,6 +119,7 @@
import org.dllearner.core.owl.ObjectProperty;
import org.dllearner.core.owl.SubClassAxiom;
import org.dllearner.kb.SparqlEndpointKS;
+import org.dllearner.kb.sparql.ExtractionDBCache;
import org.dllearner.kb.sparql.SPARQLTasks;
import org.dllearner.kb.sparql.SparqlEndpoint;
import org.dllearner.kb.sparql.SparqlKnowledgeSource;
@@ -125,7 +138,6 @@
import org.dllearner.utilities.owl.OWLAPIConverter;
import org.ini4j.IniPreferences;
import org.ini4j.InvalidFileFormatException;
-import org.semanticweb.HermiT.Reasoner;
import org.semanticweb.owlapi.apibinding.OWLManager;
import org.semanticweb.owlapi.io.RDFXMLOntologyFormat;
import org.semanticweb.owlapi.model.AxiomType;
@@ -182,13 +194,12 @@
// only axioms with a score above this threshold will be considered
private double threshold = 0.7;
- private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();
-// private SparqlEndpoint endpoint = SparqlEndpoint.getEndpointDBpedia();
+ private SparqlEndpoint endpoint;
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 0;
- private int maxDataProperties = 0;
- private int maxClasses = 0;
+ private int maxObjectProperties = 1;
+ private int maxDataProperties = 1;
+ private int maxClasses = 1;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -202,16 +213,19 @@
private OWLOntology dbPediaOntology;
private OWLReasoner reasoner;
private OWLDataFactory factory = new OWLDataFactoryImpl();
-
- public EnrichmentEvaluation() {
-
+
+ private SPARQLReasoner sparqlReasoner;
+
+ public EnrichmentEvaluation(SparqlEndpoint endpoint) {
+ this.endpoint = endpoint;
+
prefixes = new HashMap<String,String>();
prefixes.put("dbp","http://dbpedia.org/property/");
prefixes.put("dbo","http://dbpedia.org/ontology/");
prefixes.put("yago", "http://dbpedia.org/class/");
objectPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
-// objectPropertyAlgorithms.add(DisjointObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(DisjointObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(EquivalentObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(FunctionalObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(InverseFunctionalObjectPropertyAxiomLearner.class);
@@ -219,12 +233,14 @@
objectPropertyAlgorithms.add(ObjectPropertyRangeAxiomLearner.class);
objectPropertyAlgorithms.add(SubObjectPropertyOfAxiomLearner.class);
objectPropertyAlgorithms.add(SymmetricObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(AsymmetricObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(TransitiveObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(IrreflexiveObjectPropertyAxiomLearner.class);
objectPropertyAlgorithms.add(ReflexiveObjectPropertyAxiomLearner.class);
+ objectPropertyAlgorithms.add(InverseObjectPropertyAxiomLearner.class);
dataPropertyAlgorithms = new LinkedList<Class<? extends AxiomLearningAlgorithm>>();
-// dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
+ dataPropertyAlgorithms.add(DisjointDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(EquivalentDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(FunctionalDataPropertyAxiomLearner.class);
dataPropertyAlgorithms.add(DataPropertyDomainAxiomLearner.class);
@@ -301,26 +317,36 @@
}
- public void start() throws IllegalArgumentException, SecurityException, InstantiationException,
+ public void start(boolean runClassAlgorithms, boolean runObjectPropertyAlgorithms, boolean runDataPropertyAlgorithms) throws IllegalArgumentException, SecurityException, InstantiationException,
IllegalAccessException, InvocationTargetException, NoSuchMethodException,
ComponentInitException, InterruptedException {
-// dropAndCreateTable();
long overallStartTime = System.currentTimeMillis();
SparqlEndpointKS ks = new SparqlEndpointKS(endpoint);
ks.init();
-// evaluateObjectProperties(ks);
+ sparqlReasoner = new SPARQLReasoner(ks);
+ sparqlReasoner.setCache(new ExtractionDBCache("cache"));
+ sparqlReasoner.prepareSubsumptionHierarchy();
Thread.sleep(20000);
+ if(runClassAlgorithms){
+ evaluateClasses(ks);
+ }
-// evaluateDataProperties(ks);
-//
-// Thread.sleep(20000);
+ Thread.sleep(20000);
- evaluateClasses(ks);
+ if(runObjectPropertyAlgorithms){
+ evaluateObjectProperties(ks);
+ }
+ Thread.sleep(20000);
+
+ if(runDataPropertyAlgorithms){
+ evaluateDataProperties(ks);
+ }
+
System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
}
@@ -338,6 +364,7 @@
// dynamically invoke constructor with SPARQL knowledge source
AxiomLearningAlgorithm learner = algorithmClass.getConstructor(
SparqlEndpointKS.class).newInstance(ks);
+ ((AbstractAxiomLearningAlgorithm)learner).setReasoner(sparqlReasoner);
ConfigHelper.configure(learner, "propertyToDescribe", property.toString());
ConfigHelper.configure(learner, "maxExecutionTimeInSeconds",
maxExecutionTimeInSeconds);
@@ -390,7 +417,7 @@
}
}
objectProperties++;
- if (maxObjectProperties != 0 && objectProperties > maxObjectProperties) {
+ if (maxObjectProperties != 0 && objectProperties == maxObjectProperties) {
break;
}
@@ -410,11 +437,12 @@
Thread.sleep(10000);
String algName = "";
for (DatatypeProperty property : properties) {
-
+ Thread.sleep(1000);
try{
// dynamically invoke constructor with SPARQL knowledge source
AxiomLearningAlgorithm learner = algorithmClass.getConstructor(
SparqlEndpointKS.class).newInstance(ks);
+ ((AbstractAxiomLearningAlgorithm)learner).setReasoner(sparqlReasoner);
ConfigHelper.configure(learner, "propertyToDescribe", property.toString());
ConfigHelper.configure(learner, "maxExecutionTimeInSeconds",
maxExecutionTimeInSeconds);
@@ -466,7 +494,7 @@
}
}
dataProperties++;
- if (maxDataProperties != 0 && dataProperties > maxDataProperties) {
+ if (maxDataProperties != 0 && dataProperties == maxDataProperties) {
break;
}
@@ -499,6 +527,7 @@
// dynamically invoke constructor with SPARQL knowledge source
LearningAlgorithm learner = algorithmClass.getConstructor(
SparqlEndpointKS.class).newInstance(ks);
+ ((AbstractAxiomLearningAlgorithm)learner).setReasoner(sparqlReasoner);
ConfigHelper.configure(learner, "classToDescribe", cls.toString());
ConfigHelper.configure(learner, "maxExecutionTimeInSeconds",
maxExecutionTimeInSeconds);
@@ -548,7 +577,7 @@
}
classesCnt++;
- if (maxClasses != 0 && classesCnt > maxClasses) {
+ if (maxClasses != 0 && classesCnt == maxClasses) {
break;
}
@@ -689,7 +718,7 @@
int numberOfEntitiesWithTimeout = rs.getInt(1);
//compute average number of suggestions above threshold
- 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(DISTINCT 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();
@@ -734,6 +763,7 @@
}
table1.append("\\bottomrule\n\\end{tabulary}");
System.out.println(table1.toString());
+ write2Disk(table1.toString(), "evaluation/table1.tex");
//second part of evaluation
@@ -821,6 +851,7 @@
table2.append("\\end{tabulary}");
System.out.println(table2.toString());
+ write2Disk(table2.toString(), "evaluation/table2.tex");
}
private void writeToDisk(AxiomType<? extends OWLAxiom> axiomType, Map<String, Double> axiomsWithAccurracy){
@@ -862,6 +893,19 @@
}
}
+ private void write2Disk(String content, String file){
+ try {
+ new File(file).createNewFile();
+ BufferedOutputStream bos = new BufferedOutputStream(new FileOutputStream(file));
+ bos.write(content.getBytes());
+ bos.close();
+ } catch (FileNotFoundException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ }
+
private void writeToDisk(AxiomType<? extends OWLAxiom> axiomType, Set<String> axioms){
String fileName = axiomType.getName().replaceAll(" ", "_") + ".txt";
@@ -920,8 +964,10 @@
axiomType2Algorithm.put(AxiomType.FUNCTIONAL_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{FunctionalObjectPropertyAxiomLearner.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.ASYMMETRIC_OBJECT_PROPERTY, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{AsymmetricObjectPropertyAxiomLearner.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.INVERSE_OBJECT_PROPERTIES, Arrays.asList((Class<? extends LearningAlgorithm>[])new Class[]{InverseObjectPropertyAxiomLearner.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}));
@@ -1127,18 +1173,99 @@
}
public static void main(String[] args) throws Exception
-
+
{
- EnrichmentEvaluation ee = new EnrichmentEvaluation();
- ee.dropAndCreateTable();
- ee.start();
- // ee.printResultsPlain();
- ee.printResultsLaTeX();
- Files.createFile(new File("enrichment_eval.html"), ee.printHTMLTable());
- FileAppender app = new FileAppender(new SimpleLayout(), "log/enrichmentEvalErrors.log");
- Logger.getRootLogger().setLevel(Level.ERROR);
- Logger.getRootLogger().removeAllAppenders();
- Logger.getRootLogger().addAppender(app);
+ OptionParser parser = new OptionParser();
+ parser.acceptsAll(asList("h", "?", "help"), "Show help.");
+ parser.acceptsAll(asList("e", "endpoint"),
+ "SPARQL endpoint URL to be used.").withRequiredArg()
+ .ofType(URL.class);
+ parser.acceptsAll(asList("g", "graph"),
+ "URI of default graph for queries on SPARQL endpoint.")
+ .withOptionalArg().ofType(URI.class);
+ parser.acceptsAll(asList("c", "classes"),
+ "Run class axiom algorithms")
+ .withOptionalArg().ofType(Boolean.class).defaultsTo(true);
+ parser.acceptsAll(asList("o", "objectProperties"),
+ "Run object property axiom algorithms")
+ .withOptionalArg().ofType(Boolean.class).defaultsTo(true);
+ parser.acceptsAll(asList("d", "dataProperties"),
+ "Run data property axiom algorithms")
+ .withOptionalArg().ofType(Boolean.class).defaultsTo(true);
+ parser.acceptsAll(asList("drop"),
+ "Drop and create tables where data for evaluation is stored.")
+ .withOptionalArg().ofType(Boolean.class).defaultsTo(false);
+
+
+ // parse options and display a message for the user in case of problems
+ OptionSet options = null;
+ try {
+ options = parser.parse(args);
+ } catch (Exception e) {
+ System.out.println("Error: " + e.getMessage()
+ + ". Use -? to get help.");
+ System.exit(0);
+ }
+
+ // print help screen
+ if (options.has("?")) {
+ parser.printHelpOn(System.out);
+ // main script
+ } else {
+ // check that endpoint was specified
+ if (!options.hasArgument("endpoint")) {
+ System.out
+ .println("Please specify a SPARQL endpoint (using the -e option).");
+ System.exit(0);
+ }
+
+ // create SPARQL endpoint object (check that indeed a URL was given)
+ URL endpoint = null;
+ try {
+ endpoint = (URL) options.valueOf("endpoint");
+ } catch (OptionException e) {
+ System.out
+ .println("The specified endpoint appears not to be a proper URL.");
+ System.exit(0);
+ }
+ URI graph = null;
+ try {
+ graph = (URI) options.valueOf("graph");
+ } catch (OptionException e) {
+ System.out
+ .println("The specified graph appears not to be a proper URL.");
+ System.exit(0);
+ }
+
+ LinkedList<String> defaultGraphURIs = new LinkedList<String>();
+ if (graph != null) {
+ defaultGraphURIs.add(graph.toString());
+ }
+
+ SparqlEndpoint se = new SparqlEndpoint(endpoint, defaultGraphURIs,
+ new LinkedList<String>());
+
+ boolean runClassAlgorithms = (Boolean) options.valueOf("classes");
+ boolean runObjectPropertyAlgorithms = (Boolean) options.valueOf("objectProperties");
+ boolean runDataPropertyAlgorithms = (Boolean) options.valueOf("dataProperties");
+
+ boolean dropTables = (Boolean) options.valueOf("drop");
+
+ EnrichmentEvaluation ee = new EnrichmentEvaluation(se);
+ if(dropTables){
+ ee.dropAndCreateTable();
+ }
+ ee.start(runClassAlgorithms, runObjectPropertyAlgorithms, runDataPropertyAlgorithms);
+ // ee.printResultsPlain();
+ ee.printResultsLaTeX();
+ Files.createFile(new File("enrichment_eval.html"),
+ ee.printHTMLTable());
+ FileAppender app = new FileAppender(new SimpleLayout(),
+ "log/enrichmentEvalErrors.log");
+ Logger.getRootLogger().setLevel(Level.ERROR);
+ Logger.getRootLogger().removeAllAppenders();
+ Logger.getRootLogger().addAppender(app);
+ }
}
}
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|
|
From: <lor...@us...> - 2012-05-02 15:00:26
|
Revision: 3676
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3676&view=rev
Author: lorenz_b
Date: 2012-05-02 14:53:12 +0000 (Wed, 02 May 2012)
Log Message:
-----------
Modified 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 2012-05-02 14:52:45 UTC (rev 3675)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2012-05-02 14:53:12 UTC (rev 3676)
@@ -197,9 +197,9 @@
private SparqlEndpoint endpoint;
// can be used to only evaluate a part of DBpedia
- private int maxObjectProperties = 1;
- private int maxDataProperties = 1;
- private int maxClasses = 1;
+ private int maxObjectProperties = 0;
+ private int maxDataProperties = 0;
+ private int maxClasses = 0;
private List<Class<? extends AxiomLearningAlgorithm>> objectPropertyAlgorithms;
private List<Class<? extends AxiomLearningAlgorithm>> dataPropertyAlgorithms;
private List<Class<? extends LearningAlgorithm>> classAlgorithms;
@@ -328,7 +328,9 @@
sparqlReasoner = new SPARQLReasoner(ks);
sparqlReasoner.setCache(new ExtractionDBCache("cache"));
+ sparqlReasoner.setUseCache(true);
sparqlReasoner.prepareSubsumptionHierarchy();
+ sparqlReasoner.precomputeClassPopularity();
Thread.sleep(20000);
if(runClassAlgorithms){
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|
|
From: <lor...@us...> - 2012-05-02 20:44:14
|
Revision: 3679
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3679&view=rev
Author: lorenz_b
Date: 2012-05-02 20:44:08 +0000 (Wed, 02 May 2012)
Log Message:
-----------
Small modification to avoid loading YAGO schema.
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 2012-05-02 20:44:00 UTC (rev 3678)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2012-05-02 20:44:08 UTC (rev 3679)
@@ -34,7 +34,6 @@
import java.io.FileWriter;
import java.io.IOException;
import java.io.InputStream;
-import java.io.StringWriter;
import java.lang.reflect.InvocationTargetException;
import java.net.MalformedURLException;
import java.net.SocketTimeoutException;
@@ -138,6 +137,9 @@
import org.dllearner.utilities.owl.OWLAPIConverter;
import org.ini4j.IniPreferences;
import org.ini4j.InvalidFileFormatException;
+import org.semanticweb.HermiT.Configuration;
+import org.semanticweb.HermiT.Reasoner;
+import org.semanticweb.HermiT.examples.HermiTConfigurations;
import org.semanticweb.owlapi.apibinding.OWLManager;
import org.semanticweb.owlapi.io.RDFXMLOntologyFormat;
import org.semanticweb.owlapi.model.AxiomType;
@@ -1156,7 +1158,9 @@
}
}
System.out.println("Preparing reasoner ...");
-// reasoner = new Reasoner(dbPediaOntology);
+// Configuration conf = new Configuration();
+// conf.ignoreUnsupportedDatatypes = true;
+// reasoner = new Reasoner(conf, dbPediaOntology);
reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
reasoner.precomputeInferences(InferenceType.CLASS_HIERARCHY);
}
This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.
|
|
From: <lor...@us...> - 2012-05-03 14:43:15
|
Revision: 3684
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=3684&view=rev
Author: lorenz_b
Date: 2012-05-03 14:43:04 +0000 (Thu, 03 May 2012)
Log Message:
-----------
Modified 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 2012-05-03 14:42:31 UTC (rev 3683)
+++ trunk/scripts/src/main/java/org/dllearner/scripts/evaluation/EnrichmentEvaluation.java 2012-05-03 14:43:04 UTC (rev 3684)
@@ -71,6 +71,7 @@
import org.apache.commons.compress.compressors.CompressorException;
import org.apache.commons.compress.compressors.CompressorInputStream;
import org.apache.commons.compress.compressors.CompressorStreamFactory;
+import org.apache.log4j.ConsoleAppender;
import org.apache.log4j.FileAppender;
import org.apache.log4j.Level;
import org.apache.log4j.Logger;
@@ -137,18 +138,18 @@
import org.dllearner.utilities.owl.OWLAPIConverter;
import org.ini4j.IniPreferences;
import org.ini4j.InvalidFileFormatException;
-import org.semanticweb.HermiT.Configuration;
-import org.semanticweb.HermiT.Reasoner;
-import org.semanticweb.HermiT.examples.HermiTConfigurations;
import org.semanticweb.owlapi.apibinding.OWLManager;
import org.semanticweb.owlapi.io.RDFXMLOntologyFormat;
import org.semanticweb.owlapi.model.AxiomType;
import org.semanticweb.owlapi.model.IRI;
import org.semanticweb.owlapi.model.OWLAxiom;
+import org.semanticweb.owlapi.model.OWLClass;
import org.semanticweb.owlapi.model.OWLDataFactory;
+import org.semanticweb.owlapi.model.OWLDataProperty;
import org.semanticweb.owlapi.model.OWLDataPropertyAxiom;
import org.semanticweb.owlapi.model.OWLDisjointClassesAxiom;
import org.semanticweb.owlapi.model.OWLEntity;
+import org.semanticweb.owlapi.model.OWLObjectProperty;
import org.semanticweb.owlapi.model.OWLObjectPropertyAxiom;
import org.semanticweb.owlapi.model.OWLOntology;
import org.semanticweb.owlapi.model.OWLOntologyCreationException;
@@ -332,35 +333,33 @@
sparqlReasoner.setCache(new ExtractionDBCache("cache"));
sparqlReasoner.setUseCache(true);
sparqlReasoner.prepareSubsumptionHierarchy();
- sparqlReasoner.precomputeClassPopularity();
+ sparqlReasoner.precomputePopularity();
- Thread.sleep(20000);
if(runClassAlgorithms){
evaluateClasses(ks);
+ Thread.sleep(20000);
}
- Thread.sleep(20000);
-
if(runObjectPropertyAlgorithms){
evaluateObjectProperties(ks);
+ Thread.sleep(20000);
}
- Thread.sleep(20000);
-
if(runDataPropertyAlgorithms){
evaluateDataProperties(ks);
}
- System.out.println("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
+ logger.info("Overall runtime: " + (System.currentTimeMillis()-overallStartTime)/1000 + "s.");
}
private void evaluateObjectProperties(SparqlEndpointKS ks)throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<ObjectProperty> properties = new SPARQLTasks(ks.getEndpoint()).getAllObjectProperties();
+ logger.info("Evaluating " + properties.size() + " object properties...");
for (Class<? extends AxiomLearningAlgorithm> algorithmClass : objectPropertyAlgorithms) {
int objectProperties = 0;
- Thread.sleep(10000);
+ Thread.sleep(5000);
String algName = "";
for (ObjectProperty property : properties) {
@@ -383,13 +382,13 @@
while(((AbstractAxiomLearningAlgorithm)learner).isTimeout() && attempt++ < maxAttempts){
if(attempt > 1){
try {
- System.out.println("Got timeout. Waiting " + delayInMilliseconds + " ms ...");
+ logger.warn("Got timeout. Waiting " + delayInMilliseconds + " ms ...");
Thread.sleep(delayInMilliseconds);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
- System.out.println("Applying " + algName + " on " + property + " ... (Attempt " + attempt + ")");
+ logger.info("Applying " + algName + " on " + property + " ... (Attempt " + attempt + ")");
startTime = System.currentTimeMillis();
try {
learner.start();
@@ -435,10 +434,10 @@
private void evaluateDataProperties(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<DatatypeProperty> properties = new SPARQLTasks(ks.getEndpoint()).getAllDataProperties();
-
+ logger.info("Evaluating " + properties.size() + " data properties...");
for (Class<? extends AxiomLearningAlgorithm> algorithmClass : dataPropertyAlgorithms) {
int dataProperties = 0;
- Thread.sleep(10000);
+ Thread.sleep(5000);
String algName = "";
for (DatatypeProperty property : properties) {
Thread.sleep(1000);
@@ -461,12 +460,13 @@
while(((AbstractAxiomLearningAlgorithm)learner).isTimeout() && attempt++ < maxAttempts){
if(attempt > 1){
try {
+ logger.warn("Got timeout. Waiting " + delayInMilliseconds + " ms ...");
Thread.sleep(delayInMilliseconds);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
- System.out.println("Applying " + algName + " on " + property + " ... (Attempt " + attempt + ")");
+ logger.info("Applying " + algName + " on " + property + " ... (Attempt " + attempt + ")");
startTime = System.currentTimeMillis();
try {
learner.start();
@@ -511,10 +511,10 @@
private void evaluateClasses(SparqlEndpointKS ks) throws IllegalArgumentException, SecurityException, InstantiationException, IllegalAccessException, InvocationTargetException, NoSuchMethodException, ComponentInitException, InterruptedException{
Set<NamedClass> classes = new SPARQLTasks(ks.getEndpoint()).getAllClasses();
-
+ logger.info("Evaluating " + classes.size() + " classes...");
for (Class<? extends LearningAlgorithm> algorithmClass : classAlgorithms) {
int classesCnt = 0;
- Thread.sleep(10000);
+ Thread.sleep(5000);
for (NamedClass cls : classes) {
try{
@@ -524,7 +524,7 @@
String algName;
if(algorithmClass == CELOE.class){
algName = CELOE.class.getAnnotation(ComponentAnn.class).name();
- System.out.println("Applying " + algName + " on " + cls + " ... ");
+ logger.info("Applying " + algName + " on " + cls + " ... ");
learnedAxioms = applyCELOE(ks, cls, false);
} else {
@@ -545,12 +545,13 @@
while(((AbstractAxiomLearningAlgorithm)learner).isTimeout() && attempt++ < maxAttempts){
if(attempt > 1){
try {
+ logger.warn("Got timeout. Waiting " + delayInMilliseconds + " ms ...");
Thread.sleep(delayInMilliseconds);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
- System.out.println("Applying " + algName + " on " + cls + " ... (Attempt " + attempt + ")");
+ logger.info("Applying " + algName + " on " + cls + " ... (Attempt " + attempt + ")");
startTime = System.currentTimeMillis();
try {
learner.start();
@@ -783,16 +784,22 @@
// 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>();
+ Set<OWLEntity> allEntities = new HashSet<OWLEntity>();
+ Set<OWLEntity> classes = new HashSet<OWLEntity>();
+ Set<OWLEntity> objectProperties = new HashSet<OWLEntity>();
+ Set<OWLEntity> dataProperties = 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));
+ allEntities.add(factory.getOWLClass(iri));
+ classes.add(factory.getOWLClass(iri));
} else if(dbPediaOntology.containsObjectPropertyInSignature(iri)){
- entities.add(factory.getOWLObjectProperty(iri));
+ allEntities.add(factory.getOWLObjectProperty(iri));
+ objectProperties.add(factory.getOWLObjectProperty(iri));
} else if(dbPediaOntology.containsDataPropertyInSignature(iri)){
- entities.add(factory.getOWLDataProperty(iri));
+ allEntities.add(factory.getOWLDataProperty(iri));
+ dataProperties.add(factory.getOWLDataProperty(iri));
}
}
@@ -800,9 +807,17 @@
//compute recall for each axiom type
ps = conn.prepareStatement("SELECT axiom, entailed, score FROM evaluation WHERE algorithm=? AND score>=?");
+ Set<OWLEntity> entities = null;
for(Entry<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> entry : axiomType2Algorithm.entrySet()){
AxiomType<? extends OWLAxiom> type = entry.getKey();
algorithms = entry.getValue();
+ if(classAlgorithms.containsAll(algorithms)){
+ entities = classes;
+ } else if(objectPropertyAlgorithms.containsAll(algorithms)){
+ entities = objectProperties;
+ } else if(dataPropertyAlgorithms.containsAll(algorithms)){
+ entities = dataProperties;
+ }
ps.setString(1, algorithms.get(0).getAnnotation(ComponentAnn.class).name());
ps.setDouble(2, threshold);
@@ -826,10 +841,9 @@
}
//get all axioms in the reference ontology for a specific axiom type
- Set<String> relevantAxioms = getRelevantAxioms(type, entities);
+ Set<String> relevantAxioms = getRelevantAxioms2(type, entities);
//compute the axioms which are in the reference ontology, but not be computed by the learning algorithm
Set<String> missedAxioms = org.mindswap.pellet.utils.SetUtils.difference(relevantAxioms, foundAxioms);
- System.out.println(missedAxioms);
//compute the additional found axioms which were not entailed
for(String relAxiom : relevantAxioms){
foundAndNotEntailedAxioms.remove(relAxiom);
@@ -956,8 +970,8 @@
private Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> getAxiomTypesWithLearningAlgorithms(){
Map<AxiomType<? extends OWLAxiom>, List<Class<? extends LearningAlgorithm>>> axiomType2Algorithm = new LinkedHashMap<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.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}));
@@ -994,6 +1008,31 @@
return relevantAxioms;
}
+ private Set<String> getRelevantAxioms2(AxiomType<? extends OWLAxiom> axiomType, Set<OWLEntity> entities){
+ Set<String> relevantAxioms = new HashSet<String>();
+ if(entities.isEmpty()){
+ return relevantAxioms;
+ }
+ Set<OWLAxiom> entityAxioms = new HashSet<OWLAxiom>();
+ for(OWLEntity entity : entities){
+ if(entity.isOWLDataProperty()){
+ entityAxioms.addAll(dbPediaOntology.getAxioms((OWLDataProperty)entity));
+ } else if(entity.isOWLObjectProperty()){
+ entityAxioms.addAll(dbPediaOntology.getAxioms((OWLObjectProperty)entity));
+ } else if(entity.isOWLClass()){
+ entityAxioms.addAll(dbPediaOntology.getAxioms((OWLClass)entity));
+ }
+ }
+
+ for(OWLAxiom axiom : entityAxioms){
+ if(axiom.getAxiomType() == axiomType && !axiom.getClassesInSignature().contains(factory.getOWLThing())){
+ String axiomString = DLLearnerAxiomConvertVisitor.getDLLearnerAxiom(axiom).toManchesterSyntaxString(baseURI, prefixes);
+ relevantAxioms.add(axiomString);
+ }
+ }
+ return relevantAxioms;
+ }
+
private boolean isRelevantAxiom(OWLAxiom axiom, Set<OWLEntity> entities){
if(axiom instanceof OWLObjectPropertyAxiom){
return containsOneOf(axiom.getObjectPropertiesInSignature(), entities);
@@ -1139,10 +1178,10 @@
} catch (OWLOntologyCreationException e1) {
e1.printStackTrace();
} catch (FileNotFoundException e1) {
- e1.printStackTrace();
+
}
if(dbPediaOntology == null){
- System.out.println("Loading schema ...");
+ logger.info("Loading schema ...");
SPARQLReasoner r = new SPARQLReasoner(new SparqlEndpointKS(endpoint));
dbPediaOntology = convert(r.loadSchema());
try {
@@ -1157,12 +1196,14 @@
e.printStackTrace();
}
}
- System.out.println("Preparing reasoner ...");
+
+ logger.info("Preparing reasoner ...");
// Configuration conf = new Configuration();
// conf.ignoreUnsupportedDatatypes = true;
// reasoner = new Reasoner(conf, dbPediaOntology);
reasoner = PelletReasonerFactory.getInstance().createNonBufferingReasoner(dbPediaOntology);
reasoner.precomputeInferences(InferenceType.CLASS_HIERARCHY);
+ logger.info("done.");
}
private OWLOntology convert(Model model){
@@ -1178,9 +1219,24 @@
return ontology;
}
- public static void main(String[] args) throws Exception
-
- {
+ public static void main(String[] args) throws Exception {
+ Logger.getRootLogger().setLevel(Level.INFO);
+ Logger.getRootLogger().removeAllAppenders();
+
+ FileAppender app = new FileAppender(new SimpleLayout(),
+ "evaluation/errors.log");
+ app.setThreshold(Level.ERROR);
+ Logger.getRootLogger().addAppender(app);
+
+ FileAppender app2 = new FileAppender(new SimpleLayout(),
+ "evaluation/enrichment.log");
+ app2.setThreshold(Level.INFO);
+ Logger.getRootLogger().addAppender(app);
+
+ ConsoleAppender consApp = new ConsoleAppender(new SimpleLayout());
+ Logger.getRootLogger().addAppender(consApp);
+
+
OptionParser parser = new OptionParser();
parser.acceptsAll(asList("h", "?", "help"), "Show help.");
parser.acceptsAll(asList("e", "endpoint"),
@@ -1266,11 +1322,9 @@
ee.printResultsLaTeX();
Files.createFile(new File("enrichment_eval.html"),
ee.printHTMLTable());
- FileAppender app = new FileAppender(new SimpleLayout(),
- "log/enrichmentEvalErrors.log");
- Logger.getRootLogger().setLevel(Level.ERROR);
- Logger.getRootLogger().removeAllAppenders();
- Logger.getRootLogger().addAppender(app);
+
+
+
}
}
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