@SuppressWarnings("unused")
private double getInverseJaccardDistance(
TreeSet<OWLIndividual> set1, TreeSet<OWLIndividual> set2) {
Set<OWLIndividual> intersection = Helper.intersection(set1, set2);
Set<OWLIndividual> union = Helper.union(set1, set2);
return 1 - (union.size() - intersection.size()) / (double) union.size();
}
@Override
public String toString() {
String str = "";
if (nrOfRetrievals > 0) {
str += "number of retrievals: " + nrOfRetrievals + "\n";
str +=
"retrieval reasoning time: "
+ Helper.prettyPrintNanoSeconds(retrievalReasoningTimeNs)
+ " ( "
+ Helper.prettyPrintNanoSeconds(getTimePerRetrievalNs())
+ " per retrieval)"
+ "\n";
}
if (nrOfInstanceChecks > 0) {
str +=
"number of instance checks: "
+ nrOfInstanceChecks
+ " ("
+ nrOfMultiInstanceChecks
+ " multiple)\n";
str +=
"instance check reasoning time: "
+ Helper.prettyPrintNanoSeconds(instanceCheckReasoningTimeNs)
+ " ( "
+ Helper.prettyPrintNanoSeconds(getTimePerInstanceCheckNs())
+ " per instance check)\n";
}
if (nrOfSubsumptionHierarchyQueries > 0) {
str += "subsumption hierarchy queries: " + nrOfSubsumptionHierarchyQueries + "\n";
}
if (nrOfSubsumptionChecks > 0) {
str +=
"(complex) subsumption checks: "
+ nrOfSubsumptionChecks
+ " ("
+ nrOfMultiSubsumptionChecks
+ " multiple)\n";
str +=
"subsumption reasoning time: "
+ Helper.prettyPrintNanoSeconds(subsumptionReasoningTimeNs)
+ " ( "
+ Helper.prettyPrintNanoSeconds(getTimePerSubsumptionCheckNs())
+ " per subsumption check)\n";
}
str +=
"overall reasoning time: " + Helper.prettyPrintNanoSeconds(overallReasoningTimeNs) + "\n";
return str;
}
// exact computation for 5 heuristics; each one adapted to super class learning;
// each one takes the noise parameter into account
public double getAccuracyOrTooWeakExact(Description description, double noise) {
nanoStartTime = System.nanoTime();
if (heuristic.equals(HeuristicType.JACCARD)) {
// computing R(A)
TreeSet<Individual> coveredInstancesSet = new TreeSet<Individual>();
for (Individual ind : classInstances) {
if (getReasoner().hasType(description, ind)) {
coveredInstancesSet.add(ind);
}
if (terminationTimeExpired()) {
return 0;
}
}
// if even the optimal case (no additional instances covered) is not sufficient,
// the concept is too weak
if (coveredInstancesSet.size() / (double) classInstances.size() <= 1 - noise) {
return -1;
}
// computing R(C) restricted to relevant instances
TreeSet<Individual> additionalInstancesSet = new TreeSet<Individual>();
for (Individual ind : superClassInstances) {
if (getReasoner().hasType(description, ind)) {
additionalInstancesSet.add(ind);
}
if (terminationTimeExpired()) {
return 0;
}
}
Set<Individual> union = Helper.union(classInstancesSet, additionalInstancesSet);
return Heuristics.getJaccardCoefficient(coveredInstancesSet.size(), union.size());
} else if (heuristic.equals(HeuristicType.AMEASURE)
|| heuristic.equals(HeuristicType.FMEASURE)
|| heuristic.equals(HeuristicType.PRED_ACC)) {
// computing R(C) restricted to relevant instances
int additionalInstances = 0;
for (Individual ind : superClassInstances) {
if (getReasoner().hasType(description, ind)) {
additionalInstances++;
}
if (terminationTimeExpired()) {
return 0;
}
}
// computing R(A)
int coveredInstances = 0;
for (Individual ind : classInstances) {
if (getReasoner().hasType(description, ind)) {
coveredInstances++;
}
if (terminationTimeExpired()) {
return 0;
}
}
double recall = coveredInstances / (double) classInstances.size();
// noise computation is incorrect
// if(recall < 1 - noise) {
// return -1;
// }
double precision =
(additionalInstances + coveredInstances == 0)
? 0
: coveredInstances / (double) (coveredInstances + additionalInstances);
if (heuristic.equals(HeuristicType.AMEASURE)) {
// best reachable concept has same recall and precision 1:
// 1/t+1 * (t*r + 1)
if ((coverageFactor * recall + 1) / (double) (coverageFactor + 1) < (1 - noise)) {
return -1;
} else {
return Heuristics.getAScore(recall, precision, coverageFactor);
}
} else if (heuristic.equals(HeuristicType.FMEASURE)) {
// best reachable concept has same recall and precision 1:
if (((1 + Math.sqrt(coverageFactor)) * recall) / (Math.sqrt(coverageFactor) + 1)
< 1 - noise) {
return -1;
} else {
return getFMeasure(recall, precision);
}
} else if (heuristic.equals(HeuristicType.PRED_ACC)) {
if ((coverageFactor * coveredInstances + superClassInstances.size())
/ (double) (coverageFactor * classInstances.size() + superClassInstances.size())
< 1 - noise) {
return -1;
} else {
// correctly classified divided by all examples
return (coverageFactor * coveredInstances
+ superClassInstances.size()
- additionalInstances)
/ (double) (coverageFactor * classInstances.size() + superClassInstances.size());
}
}
// return heuristic.equals(HeuristicType.FMEASURE) ? getFMeasure(recall, precision) :
// getAccuracy(recall, precision);
} else if (heuristic.equals(HeuristicType.GEN_FMEASURE)) {
// implementation is based on:
// http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-426/swap2008_submission_14.pdf
// default negation should be turned off when using fast instance checker
// compute I_C (negated and non-negated concepts separately)
TreeSet<Individual> icPos = new TreeSet<Individual>();
TreeSet<Individual> icNeg = new TreeSet<Individual>();
Description descriptionNeg = new Negation(description);
// loop through all relevant instances
for (Individual ind : classAndSuperClassInstances) {
if (getReasoner().hasType(description, ind)) {
icPos.add(ind);
} else if (getReasoner().hasType(descriptionNeg, ind)) {
icNeg.add(ind);
}
if (terminationTimeExpired()) {
return 0;
}
}
// semantic precision
// first compute I_C \cap Cn(DC)
// it seems that in our setting, we can ignore Cn, because the examples (class instances)
// are already part of the background knowledge
Set<Individual> tmp1Pos = Helper.intersection(icPos, classInstancesSet);
Set<Individual> tmp1Neg = Helper.intersection(icNeg, negatedClassInstances);
int tmp1Size = tmp1Pos.size() + tmp1Neg.size();
// Cn(I_C) \cap D_C is the same set if we ignore Cn ...
int icSize = icPos.size() + icNeg.size();
double prec = (icSize == 0) ? 0 : tmp1Size / (double) icSize;
double rec = tmp1Size / (double) (classInstances.size() + negatedClassInstances.size());
// System.out.println(description);
// System.out.println("I_C pos: " + icPos);
// System.out.println("I_C neg: " + icNeg);
// System.out.println("class instances: " + classInstances);
// System.out.println("negated class instances: " + negatedClassInstances);
// System.out.println(prec);
// System.out.println(rec);
// System.out.println(coverageFactor);
// too weak: see F-measure above
// => does not work for generalised F-measure, because even very general
// concepts do not have a recall of 1
// if(((1+Math.sqrt(coverageFactor))*rec)/(Math.sqrt(coverageFactor)+1)<1-noise) {
// return -1;
// }
// we only return too weak if there is no recall
if (rec <= 0.0000001) {
return -1;
}
return getFMeasure(rec, prec);
}
throw new Error("ClassLearningProblem error: not implemented");
}
@Override
public ClassScore computeScore(Description description) {
// TODO: reuse code to ensure that we never return inconsistent results
// between getAccuracy, getAccuracyOrTooWeak and computeScore
// overhang
Set<Individual> additionalInstances = new TreeSet<Individual>();
for (Individual ind : superClassInstances) {
if (getReasoner().hasType(description, ind)) {
additionalInstances.add(ind);
}
}
// coverage
Set<Individual> coveredInstances = new TreeSet<Individual>();
for (Individual ind : classInstances) {
if (getReasoner().hasType(description, ind)) {
coveredInstances.add(ind);
}
}
double recall = coveredInstances.size() / (double) classInstances.size();
double precision =
(additionalInstances.size() + coveredInstances.size() == 0)
? 0
: coveredInstances.size()
/ (double) (coveredInstances.size() + additionalInstances.size());
// for each description with less than 100% coverage, we check whether it is
// leads to an inconsistent knowledge base
double acc = 0;
if (heuristic.equals(HeuristicType.FMEASURE)) {
acc = getFMeasure(recall, precision);
} else if (heuristic.equals(HeuristicType.AMEASURE)) {
acc = Heuristics.getAScore(recall, precision, coverageFactor);
} else {
// TODO: some superfluous instance checks are required to compute accuracy =>
// move accuracy computation here if possible
acc = getAccuracyOrTooWeakExact(description, 1);
}
if (checkConsistency) {
// we check whether the axiom already follows from the knowledge base
// boolean followsFromKB = reasoner.isSuperClassOf(description, classToDescribe);
// boolean followsFromKB = equivalence ? reasoner.isEquivalentClass(description,
// classToDescribe) : reasoner.isSuperClassOf(description, classToDescribe);
boolean followsFromKB = followsFromKB(description);
// workaround due to a bug (see
// http://sourceforge.net/tracker/?func=detail&aid=2866610&group_id=203619&atid=986319)
// boolean isConsistent = coverage >= 0.999999 || isConsistent(description);
// (if the axiom follows, then the knowledge base remains consistent)
boolean isConsistent = followsFromKB || isConsistent(description);
// double acc = useFMeasure ? getFMeasure(coverage, protusion) : getAccuracy(coverage,
// protusion);
return new ClassScore(
coveredInstances,
Helper.difference(classInstancesSet, coveredInstances),
recall,
additionalInstances,
precision,
acc,
isConsistent,
followsFromKB);
} else {
return new ClassScore(
coveredInstances,
Helper.difference(classInstancesSet, coveredInstances),
recall,
additionalInstances,
precision,
acc);
}
}
// exact computation for 5 heuristics; each one adapted to super class learning;
// each one takes the noise parameter into account
public double getAccuracyOrTooWeakExact(OWLClassExpression description, double noise) {
// System.out.println(description);
nanoStartTime = System.nanoTime();
if (heuristic.equals(HeuristicType.JACCARD)) {
// computing R(A)
TreeSet<OWLIndividual> coveredInstancesSet = new TreeSet<OWLIndividual>();
for (OWLIndividual ind : classInstances) {
if (getReasoner().hasType(description, ind)) {
coveredInstancesSet.add(ind);
}
if (terminationTimeExpired()) {
return 0;
}
}
// if even the optimal case (no additional instances covered) is not sufficient,
// the concept is too weak
if (coveredInstancesSet.size() / (double) classInstances.size() <= 1 - noise) {
return -1;
}
// computing R(C) restricted to relevant instances
TreeSet<OWLIndividual> additionalInstancesSet = new TreeSet<OWLIndividual>();
for (OWLIndividual ind : superClassInstances) {
if (getReasoner().hasType(description, ind)) {
additionalInstancesSet.add(ind);
}
if (terminationTimeExpired()) {
return 0;
}
}
Set<OWLIndividual> union = Helper.union(classInstancesSet, additionalInstancesSet);
return Heuristics.getJaccardCoefficient(coveredInstancesSet.size(), union.size());
} else if (heuristic.equals(HeuristicType.AMEASURE)
|| heuristic.equals(HeuristicType.FMEASURE)
|| heuristic.equals(HeuristicType.PRED_ACC)) {
int additionalInstances = 0;
int coveredInstances = 0;
if (reasoner.getClass().isAssignableFrom(SPARQLReasoner.class)) {
// R(C)
String query =
"SELECT (COUNT(DISTINCT(?s)) AS ?cnt) WHERE {"
+ "?s a ?sup . ?classToDescribe <http://www.w3.org/2000/01/rdf-schema#subClassOf> ?sup . "
+ converter.convert("?s", description)
+ "FILTER NOT EXISTS {?s a ?classToDescribe}}";
ParameterizedSparqlString template = new ParameterizedSparqlString(query);
// System.err.println(converter.convert("?s", description));
// template.setIri("cls", description.asOWLClass().toStringID());
template.setIri("classToDescribe", classToDescribe.toStringID());
QueryExecution qe =
((SPARQLReasoner) reasoner)
.getQueryExecutionFactory()
.createQueryExecution(template.toString());
additionalInstances = qe.execSelect().next().getLiteral("cnt").getInt();
// R(A)
OWLObjectIntersectionOf ce = df.getOWLObjectIntersectionOf(classToDescribe, description);
coveredInstances = ((SPARQLReasoner) reasoner).getPopularityOf(ce);
// System.out.println(coveredInstances);
// System.out.println(additionalInstances);
} else {
// computing R(C) restricted to relevant instances
if (useInstanceChecks) {
for (OWLIndividual ind : superClassInstances) {
if (getReasoner().hasType(description, ind)) {
additionalInstances++;
}
if (terminationTimeExpired()) {
return 0;
}
}
} else {
SortedSet<OWLIndividual> individuals = getReasoner().getIndividuals(description);
individuals.retainAll(superClassInstances);
additionalInstances = individuals.size();
}
// computing R(A)
if (useInstanceChecks) {
for (OWLIndividual ind : classInstances) {
if (getReasoner().hasType(description, ind)) {
coveredInstances++;
}
if (terminationTimeExpired()) {
return 0;
}
}
} else {
SortedSet<OWLIndividual> individuals = getReasoner().getIndividuals(description);
individuals.retainAll(classInstances);
coveredInstances = individuals.size();
}
}
// System.out.println(description + ":" + coveredInstances + "/" + classInstances.size());
double recall = coveredInstances / (double) classInstances.size();
// noise computation is incorrect
// if(recall < 1 - noise) {
// return -1;
// }
double precision =
(additionalInstances + coveredInstances == 0)
? 0
: coveredInstances / (double) (coveredInstances + additionalInstances);
if (heuristic.equals(HeuristicType.AMEASURE)) {
// best reachable concept has same recall and precision 1:
// 1/t+1 * (t*r + 1)
if ((coverageFactor * recall + 1) / (coverageFactor + 1) < (1 - noise)) {
return -1;
} else {
return Heuristics.getAScore(recall, precision, coverageFactor);
}
} else if (heuristic.equals(HeuristicType.FMEASURE)) {
// best reachable concept has same recall and precision 1:
if (((1 + Math.sqrt(coverageFactor)) * recall) / (Math.sqrt(coverageFactor) + 1)
< 1 - noise) {
return -1;
} else {
return Heuristics.getFScore(recall, precision, coverageFactor);
}
} else if (heuristic.equals(HeuristicType.PRED_ACC)) {
if ((coverageFactor * coveredInstances + superClassInstances.size())
/ (coverageFactor * classInstances.size() + superClassInstances.size())
< 1 - noise) {
return -1;
} else {
// correctly classified divided by all examples
return (coverageFactor * coveredInstances
+ superClassInstances.size()
- additionalInstances)
/ (coverageFactor * classInstances.size() + superClassInstances.size());
}
}
// return heuristic.equals(HeuristicType.FMEASURE) ? getFMeasure(recall, precision) :
// getAccuracy(recall, precision);
} else if (heuristic.equals(HeuristicType.GEN_FMEASURE)) {
// implementation is based on:
// http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-426/swap2008_submission_14.pdf
// default negation should be turned off when using fast instance checker
// compute I_C (negated and non-negated concepts separately)
TreeSet<OWLIndividual> icPos = new TreeSet<OWLIndividual>();
TreeSet<OWLIndividual> icNeg = new TreeSet<OWLIndividual>();
OWLClassExpression descriptionNeg = df.getOWLObjectComplementOf(description);
// loop through all relevant instances
for (OWLIndividual ind : classAndSuperClassInstances) {
if (getReasoner().hasType(description, ind)) {
icPos.add(ind);
} else if (getReasoner().hasType(descriptionNeg, ind)) {
icNeg.add(ind);
}
if (terminationTimeExpired()) {
return 0;
}
}
// semantic precision
// first compute I_C \cap Cn(DC)
// it seems that in our setting, we can ignore Cn, because the examples (class instances)
// are already part of the background knowledge
Set<OWLIndividual> tmp1Pos = Helper.intersection(icPos, classInstancesSet);
Set<OWLIndividual> tmp1Neg = Helper.intersection(icNeg, negatedClassInstances);
int tmp1Size = tmp1Pos.size() + tmp1Neg.size();
// Cn(I_C) \cap D_C is the same set if we ignore Cn ...
int icSize = icPos.size() + icNeg.size();
double prec = (icSize == 0) ? 0 : tmp1Size / (double) icSize;
double rec = tmp1Size / (double) (classInstances.size() + negatedClassInstances.size());
// System.out.println(description);
// System.out.println("I_C pos: " + icPos);
// System.out.println("I_C neg: " + icNeg);
// System.out.println("class instances: " + classInstances);
// System.out.println("negated class instances: " + negatedClassInstances);
// System.out.println(prec);
// System.out.println(rec);
// System.out.println(coverageFactor);
// too weak: see F-measure above
// => does not work for generalised F-measure, because even very general
// concepts do not have a recall of 1
// if(((1+Math.sqrt(coverageFactor))*rec)/(Math.sqrt(coverageFactor)+1)<1-noise) {
// return -1;
// }
// we only return too weak if there is no recall
if (rec <= 0.0000001) {
return -1;
}
return getFMeasure(rec, prec);
}
throw new Error("ClassLearningProblem error: not implemented");
}
@Override
public ClassScore computeScore(OWLClassExpression description, double noise) {
// TODO: reuse code to ensure that we never return inconsistent results
// between getAccuracy, getAccuracyOrTooWeak and computeScore
Set<OWLIndividual> additionalInstances = new TreeSet<OWLIndividual>();
Set<OWLIndividual> coveredInstances = new TreeSet<OWLIndividual>();
int additionalInstancesCnt = 0;
int coveredInstancesCnt = 0;
if (reasoner.getClass().isAssignableFrom(SPARQLReasoner.class)) {
// R(C)
String query =
"SELECT (COUNT(DISTINCT(?s)) AS ?cnt) WHERE {"
+ "?s a ?sup . ?classToDescribe <http://www.w3.org/2000/01/rdf-schema#subClassOf> ?sup . "
+ converter.convert("?s", description)
+ "FILTER NOT EXISTS {?s a ?classToDescribe}}";
ParameterizedSparqlString template = new ParameterizedSparqlString(query);
// System.err.println(converter.convert("?s", description));
// template.setIri("cls", description.asOWLClass().toStringID());
template.setIri("classToDescribe", classToDescribe.toStringID());
QueryExecution qe =
((SPARQLReasoner) reasoner)
.getQueryExecutionFactory()
.createQueryExecution(template.toString());
additionalInstancesCnt = qe.execSelect().next().getLiteral("cnt").getInt();
// R(A)
OWLObjectIntersectionOf ce = df.getOWLObjectIntersectionOf(classToDescribe, description);
coveredInstancesCnt = ((SPARQLReasoner) reasoner).getPopularityOf(ce);
} else {
// overhang
for (OWLIndividual ind : superClassInstances) {
if (getReasoner().hasType(description, ind)) {
additionalInstances.add(ind);
}
}
// coverage
for (OWLIndividual ind : classInstances) {
if (getReasoner().hasType(description, ind)) {
coveredInstances.add(ind);
}
}
additionalInstancesCnt = additionalInstances.size();
coveredInstancesCnt = coveredInstances.size();
}
double recall = coveredInstancesCnt / (double) classInstances.size();
double precision =
(additionalInstancesCnt + coveredInstancesCnt == 0)
? 0
: coveredInstancesCnt / (double) (coveredInstancesCnt + additionalInstancesCnt);
// for each OWLClassExpression with less than 100% coverage, we check whether it is
// leads to an inconsistent knowledge base
double acc = 0;
if (heuristic.equals(HeuristicType.FMEASURE)) {
acc = Heuristics.getFScore(recall, precision, coverageFactor);
} else if (heuristic.equals(HeuristicType.AMEASURE)) {
acc = Heuristics.getAScore(recall, precision, coverageFactor);
} else {
// TODO: some superfluous instance checks are required to compute accuracy =>
// move accuracy computation here if possible
acc = getAccuracyOrTooWeakExact(description, noise);
}
if (checkConsistency) {
// we check whether the axiom already follows from the knowledge base
// boolean followsFromKB = reasoner.isSuperClassOf(description, classToDescribe);
// boolean followsFromKB = equivalence ? reasoner.isEquivalentClass(description,
// classToDescribe) : reasoner.isSuperClassOf(description, classToDescribe);
boolean followsFromKB = followsFromKB(description);
// workaround due to a bug (see
// http://sourceforge.net/tracker/?func=detail&aid=2866610&group_id=203619&atid=986319)
// boolean isConsistent = coverage >= 0.999999 || isConsistent(description);
// (if the axiom follows, then the knowledge base remains consistent)
boolean isConsistent = followsFromKB || isConsistent(description);
// double acc = useFMeasure ? getFMeasure(coverage, protusion) : getAccuracy(coverage,
// protusion);
return new ClassScore(
coveredInstances,
Helper.difference(classInstancesSet, coveredInstances),
recall,
additionalInstances,
precision,
acc,
isConsistent,
followsFromKB);
} else {
return new ClassScore(
coveredInstances,
Helper.difference(classInstancesSet, coveredInstances),
recall,
additionalInstances,
precision,
acc);
}
}