OMEN: A Probabilistic OMEN: A Probabilistic Ontology Mapping ToolOntology Mapping Tool

Mitra et al.Mitra et al.

The ProblemThe Problem

We need to map databases or We need to map databases or ontologiesontologies

Mapping of two different ontologies

The ProblemThe Problem

Mapping is difficultMapping is difficult

Most mapping tools are impreciseMost mapping tools are imprecise

Even experts could be uncertainEven experts could be uncertain

We deal with probabilistic mappingsWe deal with probabilistic mappings

The SolutionThe Solution

Infer mappings based on previous onesInfer mappings based on previous ones

We use Bayesian Nets for inferenceWe use Bayesian Nets for inference

We use other tools for initial We use other tools for initial

distributionsdistributions

Preliminary results are encouragingPreliminary results are encouraging

Basic ConceptsBasic Concepts

Bayesian network:Bayesian network:

Probabilistic graphical model that Probabilistic graphical model that represents Random variablesrepresents Random variables

Evidence nodes: The value is givenEvidence nodes: The value is given

T

Bayesian NetworkBayesian Network

Conditional Probability tables (CPT)Conditional Probability tables (CPT)

Bayesian Nets in our Bayesian Nets in our approachapproach

How do we build the Bayesian NetHow do we build the Bayesian Net Nodes are property or class matchesNodes are property or class matches Classes are conceptsClasses are concepts Properties are attributes of classesProperties are attributes of classes

m(C1,C1’)C1 C1’Ontology 1 Ontology 2

Building Bayesian NetsBuilding Bayesian Nets

Our Bayesian NetsOur Bayesian Nets

All combinations of nodes is too All combinations of nodes is too manymany

We generate only “useful” nodes We generate only “useful” nodes The cutoff is k from evidence nodesThe cutoff is k from evidence nodes Up to 10 parents per nodeUp to 10 parents per node Cycles are avoided (confidence ~.5)Cycles are avoided (confidence ~.5)

Our Bayesian NetsOur Bayesian Nets

We need evidence nodes and CPTsWe need evidence nodes and CPTs

Evidence nodes come from Evidence nodes come from

initializationinitialization

CPTs come from Meta-rulesCPTs come from Meta-rules

Meta-rulesMeta-rules Describes how other rules should be usedDescribes how other rules should be used Basic Meta-ruleBasic Meta-rule

m(C1,C1’)C1 C1’

m(C2,C2’)C2 C2’

q q’

P1=x

P2=x+c

Other Meta-rulesOther Meta-rules

Range: Restriction of property valuesRange: Restriction of property values

Mappings between properties and Mappings between properties and

ranges of propertiesranges of properties

Single rangeSingle range

SpecializationSpecialization

Other Meta-rulesOther Meta-rules

Mappings between super classesMappings between super classesChildren matching depends on parents Children matching depends on parents

matchingmatching Fixed Influence Method (FI): P=.9Fixed Influence Method (FI): P=.9 Initial Probability Method (AP): P= y+cInitial Probability Method (AP): P= y+c Parent Probability Method (PP): P= x+cParent Probability Method (PP): P= x+c

Probability DistributionProbability Distribution

Probability Distribution for mapping between C and C’

Combining InfluencesCombining Influences

We assume that the parents are We assume that the parents are

conditionally independentconditionally independent

P[C|A,B] = P[C|A] x P[C|B]P[C|A,B] = P[C|A] x P[C|B]

Fix of this for future workFix of this for future work

ResultsResults

2 Sets of 11 and 19 nodes2 Sets of 11 and 19 nodes Predicate matching was manualPredicate matching was manual Thresholds were .85 and .15Thresholds were .85 and .15

ResultsResults

StrengthsStrengths

Innovative researchInnovative research

Published at ISWCPublished at ISWC

Mathematically orientedMathematically oriented

WeaknessesWeaknesses

Lots of typosLots of typos

No comparison with current methodsNo comparison with current methods

Little literature researchLittle literature research

Could use better explanation of basic Could use better explanation of basic

conceptsconcepts

Future WorkFuture Work

Handling conditionally dependency of Handling conditionally dependency of

parent nodesparent nodes

Handling of matching predicatesHandling of matching predicates

Automatic pruning and building of Automatic pruning and building of

the networkthe network

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