Download - Ontology Mapping

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Ontology Mapping

I3CON WorkshopPerMIS

August 24-26, 2004Washington D.C., USA

Marc EhrigInstitute AIFB, University of Karlsruhe

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27. Juli, 2004 Ontology Mapping 2

Agenda

• Motivation• Definitions• Mapping Process• Efficiency• Evaluation• Conclusion

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Motivation

• Semantic Web• Many individual ontologies• Distributed collaboration• Interoperability required• Automatic effective mapping necessary

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Mapping Definition

• Given two ontologies O1 and O2, mapping one ontology onto another means that for each entity (concept C, relation R, or instance I) in ontology O1, we try to find a corresponding entity, which has the same intended meaning, in ontology O2.

• map(e1i) = e2j

• Complex mappings are not addressed: n:m, concept-relation,…

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Agenda


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Process

Iterations

Input Output

Features Similarity Aggregation InterpretationEntity PairSelection

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Features

Object

Vehicle

CarBoat

hasOwner

Owner SpeedhasSpeed

Porsche KA-123Marc 250 km/h

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Similarity Measure

• String similarity

• Object Similarity

• Set similarity

)),min(

),(),min(,0max(),(

21

212121 ss

ssedsssssimString

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Similarity Rules

Feature Similarity Measure

Concepts label String Similarity

subclassOf Set Similarity

instances Set Similarity

…

Relations

Instances

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Process

Iterations

Input Output

Features Similarity Aggregation InterpretationEntity PairSelection

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Combination

• How are the individual similarity measures combined?

• Linearly• Weighted• Special Function

k

kk fesimwfesim ),(),(

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Interpretation

• From similarities to mappings

• Threshold

• map(e1j) = e2j ← sim(e1j ,e2j)>t

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Example

Object

Vehicle

CarBoat

hasOwner

OwnerSpeedhasSpeed

Porsche KA-123Marc

250 km/h

Thing

Vehicle

Automobile

Speed

hasSpecification

Marc’s Porsche fast

0.9

1.0

0.9

simLabel = 0.0simSuper = 1.0simInstance = 0.9simRelation = 0.9simCombination = 0.7

0.7

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Agenda


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Critical Operations

• Complete comparison of all entity pairs• Expensive features e.g. fetching of all

(inferred) instances of a concept• Costly heuristics e.g. Syntactic Similarity

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Assumptions

• Complete comparison unnecessary.• Complex and costly methods can in essence

be replaced by simpler methods.

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Reduction of Comparisons

• Random Selection• Closest Label• Change Propagation• Combination

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Removal of Complex Features

Feature Similarity Measure

Concepts label String Similarity

Set Similarity

Set Similarity

…

Relations

Instances

all subclassOfdirect subclassOf

all instancesdirect instances

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Complexity

• c = (feat + sel + comp · (Σk simk + agg) + inter) · iter

• NOMc = O((n + n2 + n2 ·(log2(n) + 1) + n) ·1)

= O(n2 · log2(n))

• PROMPT c = O((n + n2 + n2 ·(1 + 0) + n) ·1)

= O(n2)

• QOM c = O((n + n·log(n) + n ·(1 + 1) + n) ·1)

= O(n · log(n))

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Agenda


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Scenarios

• Travel domain: Russia• 500 entities• Manual assigned mappings by test group

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Precision

0

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0,8

1

1,2

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361

mapping with n highest similarity

p r e

c i s

i o n

Label

Sigmoid

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Recall

0

0,1

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0,9

1 20 39 58 77 96 115 134 153 172 191 210 229 248 267 286 305 324 343 362


r e c a l l

Label

Sigmoid

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F-measure

0

0,1

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0,3

0,4

0,5

0,6

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0,8

0,9

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361


f - m

e a s u

r e

Label

Sigmoid

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Efficiency

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Agenda


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Conclusion

• Automatic mappings are necessary.• Semantics help to determine better mappings.• Efficient approaches needed as ontology

numbers and size increase.• Complexity of measures can be reduced.• Number of mapping candidates can be reduced.• Loss of quality is marginal.• Good increase in efficiency.

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Outlook

• Machine learning to adapt to dynamically changing ontology environments

• Increase evaluation basis• Addition of background knowledge e.g.

WordNet• Integration into ontology applications e.g. for

merging

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Thank you.

Download - Ontology Mapping

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