eswc2009
TRANSCRIPT
ReduCE Fanizzi, d'Amato, EspositoReduCE Fanizzi, d'Amato, Esposito
ReduCE: ReduCE: A Reduced Coulomb Energy Network Method A Reduced Coulomb Energy Network Method
for Approximate Classificationfor Approximate Classification
Dipartimento di InformaticaDipartimento di InformaticaUniversità degli studi di BariUniversità degli studi di Bari
Nicola Nicola FanizziFanizziClaudia Claudia d'Amatod'AmatoFloriana Floriana EspositoEsposito
ESWC 2009ESWC 2009 22ReduCE Fanizzi, d'Amato, EspositoReduCE Fanizzi, d'Amato, Esposito
Table of ContentsTable of Contents
MotivationMotivationLearning RCE Learning RCE NetworksNetworksApproximate Classifications of IndividualsApproximate Classifications of IndividualsExperimentsExperimentsConclusions & OutlookConclusions & Outlook
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MotivationMotivation
Classic ML techniques for building inductive Classic ML techniques for building inductive classifiers for SW representations classifiers for SW representations explicitexplicit models: new concepts models: new concepts implicitimplicit models: neural networks, support vector models: neural networks, support vector
machines, graphic probabilistic modelsmachines, graphic probabilistic modelsInductive methods for classificationInductive methods for classification
often more often more efficientefficient and and noise-tolerantnoise-tolerant than standard than standard methodsmethods
enables enables approximationapproximationbetter exploitation of the inherently incomplete better exploitation of the inherently incomplete
information in Kbs for specific tasksinformation in Kbs for specific tasks
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Applications of Inductive ModelsApplications of Inductive Models
Approximate instance-checkingApproximate instance-checkingThis can be also exploited forThis can be also exploited for
approximate retrievalapproximate retrievalsubsumptionsubsumption......
It also provides alternative methods for It also provides alternative methods for ontology populationontology population
Ultimately, may be used for completing ontologies Ultimately, may be used for completing ontologies with with probabilisticprobabilistic assertions assertions enabling more sophisticate approaches to dealing with enabling more sophisticate approaches to dealing with
uncertaintyuncertainty
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Learning ProblemLearning Problem
Given a Given a target concepttarget conceptTrainTrain a model (hypothesis) a model (hypothesis) hhQQ using:using:
Set of pre-classified individuals: Set of pre-classified individuals: examplesexamplesA A knowledge baseknowledge base KK as background knowledge as background knowledge
thenthen
Use the learned model to classify all other Use the learned model to classify all other individuals:individuals:Given Given hhQQ and and xx00
Output Output hhQQ((xx00)) and possibly the likelihood of this assertionand possibly the likelihood of this assertion
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Training SetTraining Set
A A limitedlimited number of individuals number of individuals for which the intended classification is knownfor which the intended classification is known
Hypothesis: Hypothesis: the function to be approximatedthe function to be approximated
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The Inductive Model: RCE NetworksThe Inductive Model: RCE Networks
Q ¬Q
λ1 λ2 λ3 λN
x1 x2 xd
categorylayer
acj
pattern(radii)layer wjk
inputlayer
. . .
. . .
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Training the ModelTraining the Model
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RCE Model RCE Model ConstructionConstruction
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Similarity MeasureSimilarity Measure
Generalization of a pseudo-distance Generalization of a pseudo-distance dd [ESWC2008][ESWC2008]
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RCE RCE FinalFinal Model Model
Prototypes (examples)Prototypes (examples)
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((VanillaVanilla) ) ClassificationClassification Procedure Procedure
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ExtensionsExtensions
generalizing the generalizing the decision-makingdecision-making step: step:
likelihood:likelihood:
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ExperimentsExperiments
For each ontologyFor each ontology100100 satisfiable query concepts randomly satisfiable query concepts randomly generated by composition (conjunction / disjunction) of generated by composition (conjunction / disjunction) of
(2 through 8) primitive and defined concepts(2 through 8) primitive and defined conceptsEvaluationEvaluation
comparing inductive responses comparing inductive responses to those returned by a standard reasoner (Pellet 2)to those returned by a standard reasoner (Pellet 2)
IndicesIndicesmatch ratematch rate: : identical classificationidentical classificationomission error rateomission error rate: : 0 vs. 0 vs. ±±11commission error ratecommission error rate: : +1 vs. -1 or -1 vs. +1+1 vs. -1 or -1 vs. +1induction rateinduction rate: : ±±1 vs. 01 vs. 0
Several runs: rates averaged according to the Several runs: rates averaged according to the 632+ bootstrap632+ bootstrap procedure procedure
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Experiments: OntologiesExperiments: Ontologies
Ontologies employed in the experiments:Ontologies employed in the experiments:
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OutcomesOutcomes
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Outcomes / 2Outcomes / 2
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Outcomes / 3Outcomes / 3
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Conclusions & OutlookConclusions & Outlook
ML method transposed ML method transposed to SeWeb to SeWeb representationsrepresentations
Experiments: Experiments: good performancegood performanceHigh match rateHigh match rateLow induction rateLow induction rateSome omissionsSome omissionsVery limited Very limited
commissionscommissionsLow variance wrt to past Low variance wrt to past
inductive methodsinductive methods
ExtensionsExtensionsforce binary responseforce binary response
Expected to augment Expected to augment induction rateinduction rate
Pre-computation of Pre-computation of prototypical individuals:prototypical individuals:
Medoids from ClusteringMedoids from Clustering
Use likelihood for Use likelihood for rankingrankingadding probabilities to adding probabilities to
assertionsassertions
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Questions ?Questions ?
For offline contacts:For offline contacts:Nicola Nicola FanizziFanizzi [email protected]@di.uniba.itClaudia Claudia d'Amatod'Amato [email protected]@di.uniba.itFloriana Floriana EspositoEsposito [email protected]@di.uniba.it
Other methods / systemsOther methods / systemshttp://lacam.di.uniba.it:8000/~nico/research/ontologymining.html
The EndThe End