diff --git a/src/fr/inrialpes/exmo/align/impl/method/ClassStructAlignment.java b/src/fr/inrialpes/exmo/align/impl/method/ClassStructAlignment.java
index 2610f2af119cd64084afd06dbb3973970c719817..e7979c1f3592e29432d7f309e98a520e6cf926a3 100644
--- a/src/fr/inrialpes/exmo/align/impl/method/ClassStructAlignment.java
+++ b/src/fr/inrialpes/exmo/align/impl/method/ClassStructAlignment.java
@@ -144,7 +144,7 @@ public class ClassStructAlignment extends DistanceAlignment implements Alignment
// Assess factor
// -- FirstExp: nothing to be done: one pass
}
- selectBestMatch( nbclass1, classlist1, nbclass2, classlist2, classmatrix, threshold, null);
+ //selectBestMatch( nbclass1, classlist1, nbclass2, classlist2, classmatrix, threshold, null);
}
diff --git a/src/fr/inrialpes/exmo/align/impl/method/EditDistNameAlignment.java b/src/fr/inrialpes/exmo/align/impl/method/EditDistNameAlignment.java
index 704271851cc3f6504466b7841c14956dd17a103a..167904be40250652f471e4818ed5abb429db551e 100644
--- a/src/fr/inrialpes/exmo/align/impl/method/EditDistNameAlignment.java
+++ b/src/fr/inrialpes/exmo/align/impl/method/EditDistNameAlignment.java
@@ -21,12 +21,11 @@
package fr.inrialpes.exmo.align.impl.method;
import java.util.Iterator;
-import java.util.Vector;
-import java.net.URI;
import org.semanticweb.owl.model.OWLOntology;
import org.semanticweb.owl.model.OWLClass;
import org.semanticweb.owl.model.OWLProperty;
+import org.semanticweb.owl.model.OWLIndividual;
import org.semanticweb.owl.model.OWLException;
import org.semanticweb.owl.align.Alignment;
@@ -37,7 +36,6 @@ import org.semanticweb.owl.align.Parameters;
import fr.inrialpes.exmo.align.impl.DistanceAlignment;
import fr.inrialpes.exmo.align.impl.MatrixMeasure;
-import fr.inrialpes.exmo.align.impl.Similarity;
/**
* This class aligns ontology with regard to the editing distance between
@@ -48,92 +46,37 @@ import fr.inrialpes.exmo.align.impl.Similarity;
* @version $Id$
*/
-
public class EditDistNameAlignment extends DistanceAlignment implements AlignmentProcess
{
- protected final class EditDistName extends MatrixMeasure {
- public EditDistName(){
- }
- public void compute( Parameters params ){
- try {
- // Compute distances on classes
- for ( Iterator it2 = onto2.getClasses().iterator(); it2.hasNext(); ){
- OWLClass cl2 = (OWLClass)it2.next();
- int l2 = cl2.getURI().getFragment().length();
- for ( Iterator it1 = onto1.getClasses().iterator(); it1.hasNext(); ){
- OWLClass cl1 = (OWLClass)it1.next();
- int l1 = cl1.getURI().getFragment().length();
- clmatrix[((Integer)classlist1.get(cl1)).intValue()][((Integer)classlist2.get(cl2)).intValue()] =
- StringDistances.levenshteinDistance(
- cl1.getURI().getFragment().toLowerCase(),
- cl2.getURI().getFragment().toLowerCase()) / max(l1,l2);
- }
- }
- // Compute distances on properties
- for ( Iterator it2 = onto2.getObjectProperties().iterator(); it2.hasNext(); ){
- OWLProperty pr2 = (OWLProperty)it2.next();
- int l2 = pr2.getURI().getFragment().length();
- for ( Iterator it1 = onto1.getObjectProperties().iterator(); it1.hasNext(); ){
- OWLProperty pr1 = (OWLProperty)it1.next();
- int l1 = pr1.getURI().getFragment().length();
- prmatrix[((Integer)proplist1.get(pr1)).intValue()][((Integer)proplist2.get(pr2)).intValue()] =
- StringDistances.levenshteinDistance(
- pr1.getURI().getFragment().toLowerCase(),
- pr2.getURI().getFragment().toLowerCase()) / max(l1,l2);
- }
- for ( Iterator it1 = onto1.getDataProperties().iterator(); it1.hasNext(); ){
- OWLProperty pr1 = (OWLProperty)it1.next();
- int l1 = pr1.getURI().getFragment().length();
- prmatrix[((Integer)proplist1.get(pr1)).intValue()][((Integer)proplist2.get(pr2)).intValue()] =
- StringDistances.levenshteinDistance(
- pr1.getURI().getFragment().toLowerCase(),
- pr2.getURI().getFragment().toLowerCase()) / max(l1,l2);
- }
- }
- for ( Iterator it2 = onto2.getDataProperties().iterator(); it2.hasNext(); ){
- OWLProperty pr2 = (OWLProperty)it2.next();
- int l2 = pr2.getURI().getFragment().length();
- for ( Iterator it1 = onto1.getObjectProperties().iterator(); it1.hasNext(); ){
- OWLProperty pr1 = (OWLProperty)it1.next();
- int l1 = pr1.getURI().getFragment().length();
- prmatrix[((Integer)proplist1.get(pr1)).intValue()][((Integer)proplist2.get(pr2)).intValue()] =
- StringDistances.levenshteinDistance(
- pr1.getURI().getFragment().toLowerCase(),
- pr2.getURI().getFragment().toLowerCase()) / max(l1,l2);
- }
- for ( Iterator it1 = onto1.getDataProperties().iterator(); it1.hasNext(); ){
- OWLProperty pr1 = (OWLProperty)it1.next();
- int l1 = pr1.getURI().getFragment().length();
- prmatrix[((Integer)proplist1.get(pr1)).intValue()][((Integer)proplist2.get(pr2)).intValue()] =
- StringDistances.levenshteinDistance(
- pr1.getURI().getFragment().toLowerCase(),
- pr2.getURI().getFragment().toLowerCase()) / max(l1,l2);
- }
- }
-
-
- } catch (OWLException e) { e.printStackTrace(); }
- }
- }
-
/** Creation **/
public EditDistNameAlignment( OWLOntology onto1, OWLOntology onto2 ){
super( onto1, onto2 );
- setSimilarity( new EditDistName() );
- setType("**");
+ setSimilarity( new MatrixMeasure() {
+ public double measure( OWLClass cl1, OWLClass cl2 ) throws OWLException{
+ String s1 = cl1.getURI().getFragment();
+ String s2 = cl2.getURI().getFragment();
+ return StringDistances.levenshteinDistance(
+ s1.toLowerCase(),
+ s2.toLowerCase()) / max(s1.length(),s2.length());
+ }
+ public double measure( OWLProperty pr1, OWLProperty pr2 ) throws OWLException{
+ String s1 = pr1.getURI().getFragment();
+ String s2 = pr2.getURI().getFragment();
+ return StringDistances.levenshteinDistance(
+ s1.toLowerCase(),
+ s2.toLowerCase()) / max(s1.length(),s2.length());
+ }
+ public double measure( OWLIndividual id1, OWLIndividual id2 ) throws OWLException{
+ String s1 = id1.getURI().getFragment();
+ String s2 = id2.getURI().getFragment();
+ return StringDistances.levenshteinDistance(
+ s1.toLowerCase(),
+ s2.toLowerCase()) / max(s1.length(),s2.length());
+ }
+ } );
};
private double max( double i, double j) { if ( i>j ) return i; else return j; }
- /** Processing **/
- /** This is not exactly equal, this uses toLowerCase() */
- public void align( Alignment alignment, Parameters params ) throws AlignmentException, OWLException {
- //ignore alignment;
- double threshold = 1.; // threshold above which distances are to high
-
- getSimilarity().initialize( (OWLOntology)getOntology1(), (OWLOntology)getOntology2(), alignment );
- getSimilarity().compute( params );
- extract( type, params );
- }
}
diff --git a/src/fr/inrialpes/exmo/align/impl/method/NameAndPropertyAlignment.java b/src/fr/inrialpes/exmo/align/impl/method/NameAndPropertyAlignment.java
index 3397dad2ca446fd5d994ee0613330e272876a194..666e75cf1d1475cce3b7fc5cdede60caf18edd76 100644
--- a/src/fr/inrialpes/exmo/align/impl/method/NameAndPropertyAlignment.java
+++ b/src/fr/inrialpes/exmo/align/impl/method/NameAndPropertyAlignment.java
@@ -97,9 +97,6 @@ public class NameAndPropertyAlignment extends DistanceAlignment implements Align
double pia2 = 0.; // relation weight for domain
double pia3 = 0.; // relation weight for range
double epsillon = 0.05; // stoping condition
-
-
-
if ( params.getParameter("debug") != null )
debug = ((Integer)params.getParameter("debug")).intValue();
@@ -127,7 +124,6 @@ public class NameAndPropertyAlignment extends DistanceAlignment implements Align
classlist1.add( it.next() );
}
classmatrix = new double[nbclass1+1][nbclass2+1];
-
if (debug > 0) System.err.println("Initializing property distances");
for ( i=0; i<nbprop1; i++ ){
@@ -322,10 +318,7 @@ public class NameAndPropertyAlignment extends DistanceAlignment implements Align
moy=moy+max;
max=0.0;
}
-
return moy;
-
-
}
}
diff --git a/src/fr/inrialpes/exmo/align/impl/method/NameEqAlignment.java b/src/fr/inrialpes/exmo/align/impl/method/NameEqAlignment.java
index bef079432e0e6167633bdac37bf67f5f93841d9a..e9b169aa37cfc97f03a2edde736ed7d085c1731b 100644
--- a/src/fr/inrialpes/exmo/align/impl/method/NameEqAlignment.java
+++ b/src/fr/inrialpes/exmo/align/impl/method/NameEqAlignment.java
@@ -1,7 +1,7 @@
/*
* $Id$
*
- * Copyright (C) INRIA Rhône-Alpes, 2003-2004
+ * Copyright (C) INRIA Rhône-Alpes, 2003-2005
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
@@ -23,22 +23,21 @@ package fr.inrialpes.exmo.align.impl.method;
import java.util.Iterator;
import java.util.Hashtable;
-import org.semanticweb.owl.model.OWLEntity;
import org.semanticweb.owl.model.OWLOntology;
import org.semanticweb.owl.model.OWLClass;
import org.semanticweb.owl.model.OWLProperty;
import org.semanticweb.owl.model.OWLIndividual;
import org.semanticweb.owl.model.OWLException;
+import fr.inrialpes.exmo.align.impl.DistanceAlignment;
+import fr.inrialpes.exmo.align.impl.MatrixMeasure;
+import fr.inrialpes.exmo.align.impl.Similarity;
+
import org.semanticweb.owl.align.Alignment;
import org.semanticweb.owl.align.AlignmentProcess;
-import org.semanticweb.owl.align.Cell;
import org.semanticweb.owl.align.AlignmentException;
import org.semanticweb.owl.align.Parameters;
-import fr.inrialpes.exmo.align.impl.BasicAlignment;
-
-
/**
* Represents an OWL ontology alignment. An ontology comprises a number of
* collections. Each ontology has a number of classes, properties and
@@ -52,71 +51,41 @@ import fr.inrialpes.exmo.align.impl.BasicAlignment;
* @version $Id$
*/
-
-public class NameEqAlignment extends BasicAlignment implements AlignmentProcess
-{
+public class NameEqAlignment extends DistanceAlignment implements AlignmentProcess {
/** Creation **/
public NameEqAlignment( OWLOntology onto1, OWLOntology onto2 ){
- init( onto1, onto2 );
- setType("11");
+ super( onto1, onto2 );
+ setSimilarity( new MatrixMeasure() {
+ public double measure( OWLClass cl1, OWLClass cl2 ) throws OWLException{
+ String s1 = cl1.getURI().getFragment();
+ String s2 = cl2.getURI().getFragment();
+ if ( s1 != null && s2 != null && s1.toLowerCase().equals(s2.toLowerCase()) ) return 0.;
+ else return 1.;
+ }
+ public double measure( OWLProperty pr1, OWLProperty pr2 ) throws OWLException{
+ String s1 = pr1.getURI().getFragment();
+ String s2 = pr2.getURI().getFragment();
+ if ( s1 != null && s2 != null && s1.toLowerCase().equals(s2.toLowerCase()) ) return 0.;
+ else return 1.;
+ }
+ public double measure( OWLIndividual id1, OWLIndividual id2 ) throws OWLException{
+ String s1 = id1.getURI().getFragment();
+ String s2 = id2.getURI().getFragment();
+ if ( s1 != null && s2 != null && s1.toLowerCase().equals(s2.toLowerCase()) ) return 0.;
+ else return 1.;
+ }
+ } );
+ setType("**");
};
/** Processing **/
- /** This is not exactly equal, this uses toLowerCase() */
public void align( Alignment alignment, Parameters params ) throws AlignmentException, OWLException {
- Hashtable table = new Hashtable();
- OWLEntity ob1 = null;
- OWLEntity ob2 = null;
//ignore alignment;
- // This is a stupid O(2n) algorithm:
- // Put each class of onto1 in a hashtable indexed by its name (not qualified)
- // For each class of onto2 whose name is found in the hash table
- for ( Iterator it = onto1.getClasses().iterator(); it.hasNext(); ){
- ob1 = (OWLEntity)it.next();
- if ( ob1.getURI().getFragment() != null )
- table.put((Object)ob1.getURI().getFragment().toLowerCase(), ob1);
- }
- for ( Iterator it = onto2.getClasses().iterator(); it.hasNext(); ){
- ob2 = (OWLEntity)it.next();
- if ( ob2.getURI().getFragment() != null ) {
- ob1 = (OWLEntity)table.get((Object)ob2.getURI().getFragment().toLowerCase());
- if( ob1 != null ){ addAlignCell( ob1, ob2 ); }
- }
- }
- for ( Iterator it = onto1.getObjectProperties().iterator(); it.hasNext(); ){
- ob1 = (OWLEntity)it.next();
- if ( ob1.getURI().getFragment() != null )
- table.put((Object)ob1.getURI().getFragment().toLowerCase(), ob1);
- }
- for ( Iterator it = onto2.getObjectProperties().iterator(); it.hasNext(); ){
- ob2 = (OWLEntity)it.next();
- if ( ob2.getURI().getFragment() != null ){
- ob1 = (OWLEntity)table.get((Object)ob2.getURI().getFragment().toLowerCase());
- if( ob1 != null ){ addAlignCell( ob1, ob2 ); }
- }
- }
- for ( Iterator it = onto1.getDataProperties().iterator(); it.hasNext(); ){
- ob1 = (OWLEntity)it.next();
- if ( ob1.getURI().getFragment() != null )
- table.put((Object)ob1.getURI().getFragment().toLowerCase(), ob1);
- }
- for ( Iterator it = onto2.getDataProperties().iterator(); it.hasNext(); ){
- ob2 = (OWLEntity)it.next();
- if ( ob2.getURI().getFragment() != null ){
- ob1 = (OWLEntity)table.get((Object)ob2.getURI().getFragment().toLowerCase());
- if( ob1 != null ){ addAlignCell( ob1, ob2 ); }
- }
- }
- //for ( Iterator it = onto1.getIndividuals().iterator(); it.hasNext(); ){
- // id = (OWLIndividual)it.next();
- // table.put((Object)pr.getURI().getFragment().toLowerCase(), id);
- //}
- //for ( Iterator it = onto2.getIndividuals().iterator(); it.hasNext(); ){
- // OWLIndividual id2 = (OWLIndividual)it.next();
- // id = (OWLIndividual)table.get((Object)id2.getURI().getFragment().toLowerCase());
- // if( id != null ){ addAlignCell( id, id2 ); }
- // }
- }
+ double threshold = 1.; // threshold above which distances are to high
+ getSimilarity().initialize( (OWLOntology)getOntology1(), (OWLOntology)getOntology2(), alignment );
+ getSimilarity().compute( params );
+ extract( type, params );
+ }
}
diff --git a/src/fr/inrialpes/exmo/align/impl/method/SubsDistNameAlignment.java b/src/fr/inrialpes/exmo/align/impl/method/SubsDistNameAlignment.java
index 15777f1b1cf0e647ad76bb095c4619eca8078756..a77d9420e522eb2a513e3791af3cb048e1b5c65a 100644
--- a/src/fr/inrialpes/exmo/align/impl/method/SubsDistNameAlignment.java
+++ b/src/fr/inrialpes/exmo/align/impl/method/SubsDistNameAlignment.java
@@ -1,7 +1,7 @@
/*
* $Id$
*
- * Copyright (C) INRIA Rhône-Alpes, 2003-2004
+ * Copyright (C) INRIA Rhône-Alpes, 2003-2005
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
@@ -18,38 +18,30 @@
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
-
package fr.inrialpes.exmo.align.impl.method;
import java.util.Iterator;
-import java.util.Vector;
-import java.util.Set;
-import java.util.HashSet;
import org.semanticweb.owl.model.OWLOntology;
import org.semanticweb.owl.model.OWLClass;
import org.semanticweb.owl.model.OWLProperty;
-import org.semanticweb.owl.model.OWLFrame;
-import org.semanticweb.owl.model.OWLRestriction;
-import org.semanticweb.owl.model.OWLDescription;
-import org.semanticweb.owl.model.OWLNaryBooleanDescription;
+import org.semanticweb.owl.model.OWLIndividual;
import org.semanticweb.owl.model.OWLException;
-import org.semanticweb.owl.model.OWLEntity;
+
+import fr.inrialpes.exmo.align.impl.DistanceAlignment;
+import fr.inrialpes.exmo.align.impl.MatrixMeasure;
+import fr.inrialpes.exmo.align.impl.Similarity;
import org.semanticweb.owl.align.Alignment;
import org.semanticweb.owl.align.AlignmentProcess;
-import org.semanticweb.owl.align.Cell;
import org.semanticweb.owl.align.AlignmentException;
import org.semanticweb.owl.align.Parameters;
import fr.inrialpes.exmo.align.impl.DistanceAlignment;
-/** This class has been built for ISWC experiments with bibliography.
- * It implements a non iterative (one step) OLA algorithms based on
- * the name of classes and properties. It could be made iterative by
- * just adding range/domain on properties...
- * The parameters are:
- * - threshold: above what do we select for the alignment;
+/**
+ * This class implements alignment based on substring distance
+ * of class and property labels
*
* @author Jérôme Euzenat
* @version $Id$
@@ -57,119 +49,37 @@ import fr.inrialpes.exmo.align.impl.DistanceAlignment;
public class SubsDistNameAlignment extends DistanceAlignment implements AlignmentProcess
{
-
/** Creation **/
public SubsDistNameAlignment( OWLOntology onto1, OWLOntology onto2 ){
- super( onto1, onto2 );
+ super( onto1, onto2 );
+ setSimilarity( new MatrixMeasure() {
+ public double measure( OWLClass cl1, OWLClass cl2 ) throws OWLException{
+ String s1 = cl1.getURI().getFragment();
+ String s2 = cl2.getURI().getFragment();
+ return StringDistances.subStringDistance(s1.toLowerCase(),s2.toLowerCase());
+ }
+ public double measure( OWLProperty pr1, OWLProperty pr2 ) throws OWLException{
+ String s1 = pr1.getURI().getFragment();
+ String s2 = pr2.getURI().getFragment();
+ return StringDistances.subStringDistance(s1.toLowerCase(),s2.toLowerCase());
+ }
+ public double measure( OWLIndividual id1, OWLIndividual id2 ) throws OWLException{
+ String s1 = id1.getURI().getFragment();
+ String s2 = id2.getURI().getFragment();
+ return StringDistances.subStringDistance(s1.toLowerCase(),s2.toLowerCase());
+ }
+ } );
setType("**");
};
- private double max( double i, double j) { if ( i>j ) return i; else return j; }
-
/** Processing **/
public void align( Alignment alignment, Parameters params ) throws AlignmentException, OWLException {
- // The first parameter to get should be debug
//ignore alignment;
double threshold = 1.; // threshold above which distances are to high
- int i, j = 0; // index for onto1 and onto2 classes
- int l1, l2 = 0; // length of strings (for normalizing)
- int nbclass1 = 0; // number of classes in onto1
- int nbclass2 = 0; // number of classes in onto2
- Vector classlist2 = new Vector(10); // onto2 classes
- Vector classlist1 = new Vector(10); // onto1 classes
- double classmatrix[][]; // class distance matrix
- int nbprop1 = 0; // number of properties in onto1
- int nbprop2 = 0; // number of properties in onto2
- Vector proplist2 = new Vector(10); // onto2 properties
- Vector proplist1 = new Vector(10); // onto1 properties
- double propmatrix[][]; // properties distance matrix
-
- // Create property lists and matrix
- for ( Iterator it = onto1.getObjectProperties().iterator(); it.hasNext(); nbprop1++ ){
- proplist1.add( it.next() );
- }
- for ( Iterator it = onto1.getDataProperties().iterator(); it.hasNext(); nbprop1++ ){
- proplist1.add( it.next() );
- }
- for ( Iterator it = onto2.getObjectProperties().iterator(); it.hasNext(); nbprop2++ ){
- proplist2.add( it.next() );
- }
- for ( Iterator it = onto2.getDataProperties().iterator(); it.hasNext(); nbprop2++ ){
- proplist2.add( it.next() );
- }
- propmatrix = new double[nbprop1+1][nbprop2+1];
- // Create class lists
- for ( Iterator it = onto2.getClasses().iterator(); it.hasNext(); nbclass2++ ){
- classlist2.add( it.next() );
- }
- for ( Iterator it = onto1.getClasses().iterator(); it.hasNext(); nbclass1++ ){
- classlist1.add( it.next() );
- }
- classmatrix = new double[nbclass1+1][nbclass2+1];
-
- if (debug > 0) System.err.println("Initializing property distances");
- for ( i=0; i<nbprop1; i++ ){
- OWLProperty cl = (OWLProperty)proplist1.get(i);
- String s1 = cl.getURI().getFragment();
- if ( s1 != null ) s1 = s1.toLowerCase();
- for ( j=0; j<nbprop2; j++ ){
- cl = (OWLProperty)proplist2.get(j);
- String s2 = cl.getURI().getFragment();
- if ( s2 != null ) s2 = s2.toLowerCase();
- if ( s1 == null || s2 == null ) { propmatrix[i][j] = 1.; }
- else { propmatrix[i][j] = StringDistances.subStringDistance( s1, s2 ); }
- }
- }
-
- if (debug > 0) System.err.println("Initializing class distances");
- // Initialize class distances
- for ( i=0; i<nbclass1; i++ ){
- OWLClass cl = (OWLClass)classlist1.get(i);
- for ( j=0; j<nbclass2; j++ ){
- classmatrix[i][j] = StringDistances.subStringDistance(
- cl.getURI().getFragment().toLowerCase(),
- ((OWLClass)classlist2.get(j)).getURI().getFragment().toLowerCase());
- }
- }
-
- // This mechanism should be parametric!
- // Select the best match
- // There can be many algorithm for these:
- // n:m: get all of those above a threshold
- // 1:1: get the best discard lines and columns and iterate
- // Here we basically implement ?:* because the algorithm
- // picks up the best matching object above threshold for i.
- if (debug > 0) System.err.print("Storing property alignment\n");
- for ( i=0; i<nbprop1; i++ ){
- boolean found = false;
- int best = 0;
- double max = threshold;
- for ( j=0; j<nbprop2; j++ ){
- if ( propmatrix[i][j] < max) {
- found = true;
- best = j;
- max = propmatrix[i][j];
- }
- }
- if ( found ) { addAlignDistanceCell( (OWLProperty)proplist1.get(i), (OWLProperty)proplist2.get(best), "=", max ); }
- }
-
- if (debug > 0) System.err.print("Storing class alignment\n");
-
- for ( i=0; i<nbclass1; i++ ){
- boolean found = false;
- int best = 0;
- double max = threshold;
- for ( j=0; j<nbclass2; j++ ){
- if ( classmatrix[i][j] < max) {
- found = true;
- best = j;
- max = classmatrix[i][j];
- }
- }
- if ( found ) { addAlignDistanceCell( (OWLClass)classlist1.get(i), (OWLClass)classlist2.get(best), "=", max ); }
- }
+ getSimilarity().initialize( (OWLOntology)getOntology1(), (OWLOntology)getOntology2(), alignment );
+ getSimilarity().compute( params );
+ extract( type, params );
}
}
diff --git a/src/fr/inrialpes/exmo/align/ling/JWNLAlignment.java b/src/fr/inrialpes/exmo/align/ling/JWNLAlignment.java
index b946fecd2e96f38a58636863402dce7bd0282550..1a3e614309a7a0cffc09aba7dbc01b125ef09e82 100644
--- a/src/fr/inrialpes/exmo/align/ling/JWNLAlignment.java
+++ b/src/fr/inrialpes/exmo/align/ling/JWNLAlignment.java
@@ -21,16 +21,19 @@
package fr.inrialpes.exmo.align.ling;
import java.util.Iterator;
-import java.util.Vector;
-import org.semanticweb.owl.model.OWLClass;
-import org.semanticweb.owl.model.OWLException;
import org.semanticweb.owl.model.OWLOntology;
+import org.semanticweb.owl.model.OWLClass;
import org.semanticweb.owl.model.OWLProperty;
+import org.semanticweb.owl.model.OWLIndividual;
+import org.semanticweb.owl.model.OWLException;
+
+import fr.inrialpes.exmo.align.impl.DistanceAlignment;
+import fr.inrialpes.exmo.align.impl.MatrixMeasure;
+import fr.inrialpes.exmo.align.impl.Similarity;
import org.semanticweb.owl.align.Alignment;
import org.semanticweb.owl.align.AlignmentProcess;
-import org.semanticweb.owl.align.Cell;
import org.semanticweb.owl.align.AlignmentException;
import org.semanticweb.owl.align.Parameters;
@@ -42,132 +45,48 @@ import fr.inrialpes.exmo.align.impl.DistanceAlignment;
* @version $Id: JWNLAlignment.java,v 1.0 2004/08/04
*/
-
public class JWNLAlignment extends DistanceAlignment implements AlignmentProcess
{
+ protected class SynonymMatrixMeasure extends MatrixMeasure {
+ protected JWNLDistances Dist = null;
+
+ public SynonymMatrixMeasure() {
+ Dist = new JWNLDistances();
+ Dist.Initialize();
+ }
+ public double measure( OWLClass cl1, OWLClass cl2 ) throws OWLException{
+ String s1 = cl1.getURI().getFragment();
+ String s2 = cl2.getURI().getFragment();
+ return Dist.BasicSynonymDistance(s1.toLowerCase(),s2.toLowerCase());
+ }
+ public double measure( OWLProperty pr1, OWLProperty pr2 ) throws OWLException{
+ String s1 = pr1.getURI().getFragment();
+ String s2 = pr2.getURI().getFragment();
+ return Dist.BasicSynonymDistance(s1.toLowerCase(),s2.toLowerCase());
+ }
+ public double measure( OWLIndividual id1, OWLIndividual id2 ) throws OWLException{
+ String s1 = id1.getURI().getFragment();
+ String s2 = id2.getURI().getFragment();
+ return Dist.BasicSynonymDistance(s1.toLowerCase(),s2.toLowerCase());
+ }
+ }
+
/** Creation **/
public JWNLAlignment( OWLOntology onto1, OWLOntology onto2 ){
super( onto1, onto2 );
+ setSimilarity( new SynonymMatrixMeasure() );
setType("**");
};
- private double max( double i, double j) { if ( i>j ) return i; else return j; }
-
/** Processing **/
public void align( Alignment alignment, Parameters params ) throws AlignmentException, OWLException {
//ignore alignment;
- double threshold = 1.; // threshold above which distances are too high
- int i, j, k = 0; // index for onto1 and onto2 classes
- int l1, l2 = 0; // length of strings (for normalizing)
- int nbclass1 = 0; // number of classes in onto1
- int nbclass2 = 0; // number of classes in onto2
- Vector classlist2 = new Vector(10); // onto2 classes
- Vector classlist1 = new Vector(10); // onto1 classes
- double classmatrix[][]; // class distance matrix
- int nbprop1 = 0; // number of properties in onto1
- int nbprop2 = 0; // number of properties in onto2
- Vector proplist2 = new Vector(10); // onto2 properties
- Vector proplist1 = new Vector(10); // onto1 properties
-
- double propmatrix[][]; // properties distance matrix
- double pic1 = 0.5; // class weigth for name
- double pic2 = 0.5; // class weight for properties
- double pia1 = 1.; // relation weight for name
- double pia2 = 0.; // relation weight for domain
- double pia3 = 0.; // relation weight for range
- double epsillon = 0.05; // stoping condition
- // JE: changed within the tutorial Old to not-ald
- JWNLDistances Dist = new JWNLDistances();
- //OldJWNLDistances Dist = new OldJWNLDistances();
- Dist.Initialize();
- if ( params.getParameter("debug") != null )
- debug = ((Integer)params.getParameter("debug")).intValue();
- // Create property lists and matrix
- for ( Iterator it = onto1.getObjectProperties().iterator(); it.hasNext(); nbprop1++ ){
- proplist1.add( it.next() );
- }
- for ( Iterator it = onto1.getDataProperties().iterator(); it.hasNext(); nbprop1++ ){
- proplist1.add( it.next() );
- }
- for ( Iterator it = onto2.getObjectProperties().iterator(); it.hasNext(); nbprop2++ ){
- proplist2.add( it.next() );
- }
- for ( Iterator it = onto2.getDataProperties().iterator(); it.hasNext(); nbprop2++ ){
- proplist2.add( it.next() );
- }
- propmatrix = new double[nbprop1+1][nbprop2+1];
-
- // Create class lists
- for ( Iterator it = onto2.getClasses().iterator(); it.hasNext(); nbclass2++ ){
- classlist2.add( it.next() );
- }
- for ( Iterator it = onto1.getClasses().iterator(); it.hasNext(); nbclass1++ ){
- classlist1.add( it.next() );
- }
- classmatrix = new double[nbclass1+1][nbclass2+1];
-
-
- if (debug > 0) System.err.println("Initializing property distances");
- for ( i=0; i<nbprop1; i++ ){
- OWLProperty cl = (OWLProperty)proplist1.get(i);
- String st1=new String();
- String st2=new String();
- if (cl.getURI().getFragment()!=null){ st1 = cl.getURI().getFragment().toLowerCase();}
- for ( j=0; j<nbprop2; j++ ){
- cl = (OWLProperty)proplist2.get(j);
- if(cl.getURI().getFragment()!=null){st2 = cl.getURI().getFragment().toLowerCase() ;}
- propmatrix[i][j] = Dist.BasicSynonymDistance(st1,st2);
- }
- }
- if (debug > 0) System.err.println("Initializing class distances");
- for ( i=0; i<nbclass1; i++ ){
- OWLClass cl = (OWLClass)classlist1.get(i);
- for ( j=0; j<nbclass2; j++ ){
- classmatrix[i][j] = Dist.BasicSynonymDistance(
- cl.getURI().getFragment().toLowerCase(),
- ((OWLClass)classlist2.get(j)).getURI().getFragment().toLowerCase());
- }
- }
-
- // This mechanism should be parametric!
- // Select the best match
- // There can be many algorithm for these:
- // n:m: get all of those above a threshold
- // 1:1: get the best discard lines and columns and iterate
- // Here we basically implement ?:* because the algorithm
- // picks up the best matching object above threshold for i.
- if (debug > 0) System.err.print("Storing property alignment\n");
- for ( i=0; i<nbprop1; i++ ){
- boolean found = false;
- int best = 0;
- double max = threshold;
- for ( j=0; j<nbprop2; j++ ){
- if ( propmatrix[i][j] < max) {
- found = true;
- best = j;
- max = propmatrix[i][j];
- }
- }
- if ( found ) {addAlignDistanceCell( (OWLProperty)proplist1.get(i), (OWLProperty)proplist2.get(best), "=", max ); }
- }
-
- if (debug > 0) System.err.print("Storing class alignment\n");
+ double threshold = 1.; // threshold above which distances are to high
- for ( i=0; i<nbclass1; i++ ){
- boolean found = false;
- int best = 0;
- double max = threshold;
- for ( j=0; j<nbclass2; j++ ){
- if ( classmatrix[i][j] < max) {
- found = true;
- best = j;
- max = classmatrix[i][j];
- }
- }
- if ( found ) { addAlignDistanceCell( (OWLClass)classlist1.get(i), (OWLClass)classlist2.get(best), "=", max ); }
- }
+ getSimilarity().initialize( (OWLOntology)getOntology1(), (OWLOntology)getOntology2(), alignment );
+ getSimilarity().compute( params );
+ extract( type, params );
}
-
}