Ontology Mapping
description
Transcript of 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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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
• Motivation• Definitions• Mapping Process• Efficiency• Evaluation• Conclusion
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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(),(
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212121 ss
ssedsssssimString
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Similarity Rules
Feature Similarity Measure
Concepts label String Similarity
subclassOf Set Similarity
instances Set Similarity
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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
• Motivation• Definitions• Mapping Process• Efficiency• Evaluation• Conclusion
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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
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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
• Motivation• Definitions• Mapping Process• Efficiency• Evaluation• Conclusion
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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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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
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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
r e c a l l
Label
Sigmoid
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F-measure
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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 - m
e a s u
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Label
Sigmoid
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Efficiency
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Agenda
• Motivation• Definitions• Mapping Process• Efficiency• Evaluation• Conclusion
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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.