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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

slide29
Thank you.

Ontology Mapping

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