1. AIFB Ontology Mapping I3CON Workshop PerMIS August 24-26, 2004 Washington D.C., USA Marc Ehrig Institute AIFB, University of Karlsruhe

2. AIFB 27. Juli, 2004Ontology Mapping2 Agenda Motivation Definitions Mapping Process Efficiency Evaluation Conclusion

3. AIFB 27. Juli, 2004Ontology Mapping3 Motivation Semantic Web Many individual ontologies Distributed collaboration Interoperability required Automatic effective mapping necessary

4. AIFB 27. Juli, 2004Ontology Mapping4 Mapping Definition Given two ontologies O 1 and O 2, mapping one ontology onto another means that for each entity (concept C, relation R, or instance I) in ontology O 1, we try to find a corresponding entity, which has the same intended meaning, in ontology O 2. map(e 1i ) = e 2j Complex mappings are not addressed: n:m, concept-relation,…

5. AIFB 27. Juli, 2004Ontology Mapping5 Agenda Motivation Definitions Mapping Process Efficiency Evaluation Conclusion

6. AIFB 27. Juli, 2004Ontology Mapping6 Process Iterations Input Output FeaturesSimilarityAggregationInterpretation Entity Pair Selection

7. AIFB 27. Juli, 2004Ontology Mapping7 Features Object Vehicle CarBoat hasOwner Owner Speed hasSpeed Porsche KA-123 Marc 250 km/h

8. AIFB 27. Juli, 2004Ontology Mapping8 Similarity Measure String similarity Object Similarity Set similarity

9. AIFB 27. Juli, 2004Ontology Mapping9 Similarity Rules FeatureSimilarity Measure ConceptslabelString Similarity subclassOfSet Similarity instancesSet Similarity … Relations Instances

10. AIFB 27. Juli, 2004Ontology Mapping10 Process Iterations Input Output FeaturesSimilarityAggregationInterpretation Entity Pair Selection

11. AIFB 27. Juli, 2004Ontology Mapping11 Combination How are the individual similarity measures combined? Linearly Weighted Special Function

12. AIFB 27. Juli, 2004Ontology Mapping12 Interpretation From similarities to mappings Threshold map(e 1j ) = e 2j ← sim(e 1j,e 2j )>t

13. AIFB 27. Juli, 2004Ontology Mapping13 Example Object Vehicle Car Boat hasOwner Owner SpeedhasSpeed Porsche KA-123 Marc 250 km/h Thing Vehicle Automobile Speed hasSpecification Marc’s Porschefast 0.9 1.0 0.9 sim Label = 0.0 sim Super = 1.0 sim Instance = 0.9 sim Relation = 0.9 sim Combination = 0.7 0.7

14. AIFB 27. Juli, 2004Ontology Mapping14 Agenda Motivation Definitions Mapping Process Efficiency Evaluation Conclusion

15. AIFB 27. Juli, 2004Ontology Mapping15 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

16. AIFB 27. Juli, 2004Ontology Mapping16 Assumptions Complete comparison unnecessary. Complex and costly methods can in essence be replaced by simpler methods.

17. AIFB 27. Juli, 2004Ontology Mapping17 Reduction of Comparisons Random Selection Closest Label Change Propagation Combination

18. AIFB 27. Juli, 2004Ontology Mapping18 Removal of Complex Features FeatureSimilarity Measure ConceptslabelString Similarity Set Similarity … Relations Instances all subclassOfdirect subclassOf all instancesdirect instances

19. AIFB 27. Juli, 2004Ontology Mapping19 Complexity c = (feat + sel + comp · (Σ k sim k + agg) + inter) · iter NOM c = O((n + n 2 + n 2 ·(log 2 (n) + 1) + n) ·1) = O(n 2 · log 2 (n)) PROMPT c = O((n + n 2 + n 2 ·(1 + 0) + n) ·1) = O(n 2 ) QOM c = O((n + n·log(n) + n ·(1 + 1) + n) ·1) = O(n · log(n))

20. AIFB 27. Juli, 2004Ontology Mapping20 Agenda Motivation Definitions Mapping Process Efficiency Evaluation Conclusion

21. AIFB 27. Juli, 2004Ontology Mapping21 Scenarios Travel domain: Russia 500 entities Manual assigned mappings by test group

22. AIFB 27. Juli, 2004Ontology Mapping22 Precision

23. AIFB 27. Juli, 2004Ontology Mapping23 Recall

24. AIFB 27. Juli, 2004Ontology Mapping24 F-measure

25. AIFB 27. Juli, 2004Ontology Mapping25 Efficiency

26. AIFB 27. Juli, 2004Ontology Mapping26 Agenda Motivation Definitions Mapping Process Efficiency Evaluation Conclusion

27. AIFB 27. Juli, 2004Ontology Mapping27 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.

28. AIFB 27. Juli, 2004Ontology Mapping28 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

29. AIFB 27. Juli, 2004Ontology Mapping29 Thank you.