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Ontology Mapping. I3CON Workshop PerMIS August 24-26, 2004 Washington D.C., USA Marc Ehrig Institute AIFB, University of Karlsruhe. Agenda. Motivation Definitions Mapping Process Efficiency Evaluation Conclusion. Motivation. Semantic Web Many individual ontologies

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Ontology mapping

Ontology Mapping

I3CON Workshop

PerMIS

August 24-26, 2004

Washington D.C., USA

Marc Ehrig

Institute AIFB, University of Karlsruhe


Agenda
Agenda

  • Motivation

  • Definitions

  • Mapping Process

  • Efficiency

  • Evaluation

  • Conclusion

Ontology Mapping


Motivation
Motivation

  • Semantic Web

  • Many individual ontologies

  • Distributed collaboration

  • Interoperability required

  • Automatic effective mapping necessary

Ontology Mapping


Mapping definition
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,…

Ontology Mapping


Agenda1
Agenda

  • Motivation

  • Definitions

  • Mapping Process

  • Efficiency

  • Evaluation

  • Conclusion

Ontology Mapping


Process

Iterations

Input

Process

Entity Pair

Selection

Features

Similarity

Aggregation

Interpretation

Output

Ontology Mapping


Features
Features

Object

Vehicle

hasOwner

hasSpeed

Boat

Car

Speed

Owner

250 km/h

Marc

Porsche KA-123

Ontology Mapping


Similarity measure
Similarity Measure

  • String similarity

  • Object Similarity

  • Set similarity

Ontology Mapping


Similarity rules
Similarity Rules

Ontology Mapping


Process1

Iterations

Input

Process

Entity Pair

Selection

Features

Similarity

Aggregation

Interpretation

Output

Ontology Mapping


Combination
Combination

  • How are the individual similarity measures combined?

  • Linearly

  • Weighted

  • Special Function

Ontology Mapping


Interpretation
Interpretation

  • From similarities to mappings

  • Threshold

  • map(e1j) = e2j ← sim(e1j ,e2j)>t

Ontology Mapping


Example

Thing

Vehicle

simLabel = 0.0

simSuper = 1.0

1.0

simInstance = 0.9

hasSpecification

Automobile

simRelation = 0.9

Speed

simCombination = 0.7

Object

Marc’s Porsche

fast

0.7

Vehicle

hasOwner

0.9

Boat

0.9

Owner

Car

hasSpeed

Speed

Marc

Porsche KA-123

250 km/h

Example

Ontology Mapping


Agenda2
Agenda

  • Motivation

  • Definitions

  • Mapping Process

  • Efficiency

  • Evaluation

  • Conclusion

Ontology Mapping


Critical operations
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

Ontology Mapping


Assumptions
Assumptions

  • Complete comparison unnecessary.

  • Complex and costly methods can in essence be replaced by simpler methods.

Ontology Mapping


Reduction of comparisons
Reduction of Comparisons

  • Random Selection

  • Closest Label

  • Change Propagation

  • Combination

Ontology Mapping


Removal of complex features
Removal of Complex Features

direct subclassOf

all subclassOf

direct instances

all instances

Ontology Mapping


Complexity
Complexity

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

  • NOM

    c = 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))

Ontology Mapping


Agenda3
Agenda

  • Motivation

  • Definitions

  • Mapping Process

  • Efficiency

  • Evaluation

  • Conclusion

Ontology Mapping


Scenarios
Scenarios

  • Travel domain: Russia

  • 500 entities

  • Manual assigned mappings by test group

Ontology Mapping


Precision

1,2

Label

Sigmoid

1

0,8

n

o

i

s

i

0,6

c

e

r

p

0,4

0,2

0

1

21

41

61

81

101

121

141

161

181

201

221

241

261

281

301

321

341

361

mapping with n highest similarity

Precision

Ontology Mapping


Recall

0,9

0,8

0,7

0,6

0,5

l

l

a

c

e

r

0,4

Label

0,3

Sigmoid

0,2

0,1

0

1

20

39

58

77

96

115

134

153

172

191

210

229

248

267

286

305

324

343

362

mapping with n highest similarity

Recall

Ontology Mapping


F measure

0,9

0,8

0,7

0,6

e

r

0,5

u

s

a

e

m

0,4

-

f

Label

0,3

Sigmoid

0,2

0,1

0

1

21

41

61

81

101

121

141

161

181

201

221

241

261

281

301

321

341

361

mapping with n highest similarity

F-measure

Ontology Mapping


Efficiency
Efficiency

Ontology Mapping


Agenda4
Agenda

  • Motivation

  • Definitions

  • Mapping Process

  • Efficiency

  • Evaluation

  • Conclusion

Ontology Mapping


Conclusion
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.

Ontology Mapping


Outlook
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

Ontology Mapping


Thank you.

Ontology Mapping


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