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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. Motivation Definitions Mapping Process Efficiency Evaluation Conclusion. Motivation. Semantic Web Many individual ontologies

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Ontology Mapping

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

  2. Agenda • Motivation • Definitions • Mapping Process • Efficiency • Evaluation • Conclusion Ontology Mapping

  3. Motivation • Semantic Web • Many individual ontologies • Distributed collaboration • Interoperability required • Automatic effective mapping necessary Ontology Mapping

  4. 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

  5. Agenda • Motivation • Definitions • Mapping Process • Efficiency • Evaluation • Conclusion Ontology Mapping

  6. Iterations Input Process Entity Pair Selection Features Similarity Aggregation Interpretation Output Ontology Mapping

  7. Features Object Vehicle hasOwner hasSpeed Boat Car Speed Owner 250 km/h Marc Porsche KA-123 Ontology Mapping

  8. Similarity Measure • String similarity • Object Similarity • Set similarity Ontology Mapping

  9. Similarity Rules Ontology Mapping

  10. Iterations Input Process Entity Pair Selection Features Similarity Aggregation Interpretation Output Ontology Mapping

  11. Combination • How are the individual similarity measures combined? • Linearly • Weighted • Special Function Ontology Mapping

  12. Interpretation • From similarities to mappings • Threshold • map(e1j) = e2j ← sim(e1j ,e2j)>t Ontology Mapping

  13. 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

  14. Agenda • Motivation • Definitions • Mapping Process • Efficiency • Evaluation • Conclusion Ontology Mapping

  15. 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

  16. Assumptions • Complete comparison unnecessary. • Complex and costly methods can in essence be replaced by simpler methods. Ontology Mapping

  17. Reduction of Comparisons • Random Selection • Closest Label • Change Propagation • Combination Ontology Mapping

  18. Removal of Complex Features direct subclassOf all subclassOf direct instances all instances Ontology Mapping

  19. 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

  20. Agenda • Motivation • Definitions • Mapping Process • Efficiency • Evaluation • Conclusion Ontology Mapping

  21. Scenarios • Travel domain: Russia • 500 entities • Manual assigned mappings by test group Ontology Mapping

  22. 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

  23. 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

  24. 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

  25. Efficiency Ontology Mapping

  26. Agenda • Motivation • Definitions • Mapping Process • Efficiency • Evaluation • Conclusion Ontology Mapping

  27. 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

  28. 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

  29. Thank you. Ontology Mapping

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