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Matching Domain Ontologies A Comparative Study [Mode De Compatibilité]

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it's our presentation at CISIS 2009, International Conference on Complex, Intelligent and Software Intensive Systems held in Fukuoka, Japan …

it's our presentation at CISIS 2009, International Conference on Complex, Intelligent and Software Intensive Systems held in Fukuoka, Japan
March 16-March 19 2009.

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  • 1. Mrs Leila Ghomari-Zemmouchi Ghomari- L_zemmouchi@esi.dz January, 31st 2009 January,
  • 2. Program Presentation Contexte Context : Ontology Heterogeneity Matching Ontologies systems Solution : selection selection Ontology Matching Matching Research ontologies : Problem & 1st Matching Objectives 2nd Matching 3rd Matching Synthesis Evaluation Of obtained of results alignments 2
  • 3. Context Ontological Engineering Data Integration Need : P2P Information Ontology sharing Simultaneous Utilization Web Services Composition Multi-Agent Communication 3
  • 4. Contexte Context : Ontology Heterogeneity 1. Syntactic Level . 2. Terminological Level 3. Conceptual Level 4. Semiotic Level 4
  • 5. Context : Ontology Heterogeneity Ontology Matching process of corresponding semantically Entities which compose ontologies 5
  • 6. Context : Ontology Heterogeneity Adapted from [Isaac, 2007] 6
  • 7. Context : Ontology Heterogeneity Adapted from [Isaac, 2007] 7
  • 8. Program Presentation Contexte Context : Ontology Heterogeneity Matching Ontologies systems Solution : selection selection Ontology Matching Matching Research ontologies : Problem & 1st Matching Objectives 2nd Matching 3rd Matching Synthesis Evaluation Of obtained of results alignments 8
  • 9. Research Problem & Objectives 9
  • 10. Research Problem & Objectives Since 2004 I3CON EON Ontology Information Evaluation Alignment Interpretation of ONtology Evaluation and Integration Tools Initiative Conference 10
  • 11. Research Problem & Objectives Matching Systems H-Match Cupid Artemis Wise-Integrator TranScm Tess Anchor-Prompt SKAT DIKE OMEN RiMOM ASCO BayesOWL Hovy Similarity flooding OntoMerge MapOnto OLA MoA Clio Automatch HCONE Falcon-AO Dumas DELTA oMap LSD/GLUE/iMAP sPLMap ToMAS FCA-merge SEMINT XClust IF-Map CAIMAN SBI&NB Xu & al. S-Match Kang & Naughton COMA & COMA++ OntoBuilder Wang & al. DCM CtxMatch NOM & QOM T-tree Corpus-based 11 matching
  • 12. Research Problem & Objectives “Ontologies are formal representations of semantics” semantics” [Guarino, 1995] Guarino, 12
  • 13. Research Problem & Objectives Syntaxic Systems Semantic Systems T-tree S-Match Wise-Integrator CtxMatch Anchor-Prompt OMEN BayesOWL OntoMerge MoA HCONE DELTA sPLMap SEMINT CAIMAN COMA & COMA++ OntoBuilder OLA 13
  • 14. Research Problem & Objectives 14
  • 15. Research Problem & Objectives Identify selected matching systems strengths and weaknesses in order to improve their matching quality. 1 Contribute to analyze the progress of both semantic syntactic matching systems 2 Help future matching systems developers to select the adequate approach matching 3 15
  • 16. Program Presentation Contexte Context : Ontology Heterogeneity Matching Ontologies systems Solution : selection selection Ontology Matching Matching Research ontologies : Problem & 1st Matching Objectives 2nd Matching 3rd Matching Synthesis Evaluation Of obtained of results alignments 16
  • 17. Ontologies Selection To To Evaluate To Achieve Undestand To be an a reference ontologies Ontologies automatic matching to be Domain(s) matching which is Experts matched result manual very well 17
  • 18. Ontologies Selection Source : [Sean & al.], OM 2007 18
  • 19. Ontologies Selection Ontology URI University Origin O1 http://www.mindswap.org/2005/debugging/ontologies/ University.owl O2 http://www.lehigh.edu/~zhp2/2004/0401/ univ-bench.owl O3 http://www.webkursi.lv/luweb05fall/resources/ university.owl 19
  • 20. Ontologies Selection Ontologies Classes Properties Restrictions Instances Language O1 30 12 18 4 OWL - FULL O2 43 31 8 0 OWL - DL O3 73 46 33 80 OWL - FULL 20
  • 21. Program Presentation Contexte Context : Ontology Heterogeneity Matching Ontologies systems Solution : selection selection Ontology Matching Matching Research ontologies : Problem & 1st Matching Objectives 2nd Matching 3rd Matching Synthesis Evaluation Of obtained of results alignments 21
  • 22. Matching Systems Selection CTXMatch 2003 S-Match 2004 (not available) CTXMatch 2 OWL-CTXMatch 2006 2006 [Bouquet & al., 2006] Trento University, Italy 22
  • 23. Matching Systems Selection COmbination of schema MAtching Dumas Wang & al. SEMINT DELTA MapOnto XClust Approaches [Aumueller & al., 2005] Clio GLUE DCM Leipzieg U., Germany CAIMAN Hovy SBI&NB Kang & Cupid Falcon-AO FCA-merge T-tree QOM Naughton COMA++ Wise- TranScm oMap IF-Map OntoBuilder Integrator SKAT ASCO Xu & al. BayesOWL ToMAS Similarity Corpus-based RiMOM NOM Anchor-Prompt OntoMerge flooding matching Tess H-Match OLA OMEN MoA LSD DIKE Artemis Automatch HCONE sPLMap IMAP 23
  • 24. Matching Systems Selection 24
  • 25. Matching Systems Selection Internal representation Semantic Construction (Form : Elicitation description logic formulas) The reasoner merge Formulas sets in one model, classify and Automaic determine which Deduction of relation type relationships associates the two between entities entities (=, ∩,⊆,⊇,⊥) by a reasoner OWL-CTXMatch 25
  • 26. Matching Systems Selection Matchers Schemas Definition and Manipulation Execution (Entity1, Entity2, Matcher)= COMA++ Similarity value Similarity Cube Where user can modify the default configuration Direction Agreggation Combination Selection 26
  • 27. Program Presentation Contexte Context : Ontology Heterogeneity Matching Ontologies systems Solution : selection selection Ontology Matching Matching Research ontologies : Problem & 1st Matching Objectives 2nd Matching 3rd Matching Synthesis Evaluation Of obtained of results alignments 27
  • 28. Matching Ontologies O1 3rd Matching 1st Matching O3 O2 2nd Matching 28
  • 29. Matching Ontologies 29
  • 30. Matching Ontologies 30
  • 31. Matching Ontologies 31
  • 32. Matching Ontologies 32
  • 33. Matching Ontologies 33
  • 34. Matching Ontologies Intuition Lexical Thesauri such as : Wordnet Expert Domain knowledge Ontologies 34
  • 35. Matching Ontologies Reference Reference Reference Matching Matching Matching O1 O2 O2 O3 O3 O1 215 662 600 Correspondences Correspondences Correspondences 6.9% 7.5% 12% of all of all of all correspondences correspondences correspondences 35
  • 36. Program Presentation Contexte Context : Ontology Heterogeneity Matching Ontologies systems Solution : selection selection Ontology Matching Matching Research ontologies : Problem & 1st Matching Objectives 2nd Matching 3rd Matching Synthesis Evaluation Of obtained of results alignments 36
  • 37. Matching Evaluation EXPERT Precision = TP/TP+FP Recall = TP/TP+FN TN FN 2 * Recall * Precision F-Mesure = Recall + Precision TP FP Overall = Recall (2-(1/Precision)) AUTOMATIC SYSTEM 37
  • 38. Matching Evaluation 38
  • 39. Matching Evaluation 39
  • 40. Matching Evaluation 40
  • 41. Matching Evaluation 41
  • 42. Matching Evaluation 42
  • 43. Program Presentation Contexte Context : Ontology Heterogeneity Matching Ontology systems Solution : selection selection Ontology Matching Matching Research ontologies : Problem & 1st Matching Objectives 2nd Matching 3rd Matching Synthesis Evaluation Of obtained of results alignments 43
  • 44. Synthesis of obtained results Class Class Property Property 44
  • 45. Synthesis of obtained results 77 Classe Classe 45 32 Propriété Propriété 0.12 0.08 0.04 45
  • 46. Synthesis of obtained results Measuring Unit : second 46
  • 47. Synthesis of obtained results 47
  • 48. Synthesis of obtained results 48
  • 49. Synthesis of obtained results Few Common Few Common alignments alignments Few Common alignments 49
  • 50. CONCLUSION • The two matching dimensions must be taken About into account : Matching syntactic (Matching terms) AND terms) Approaches semantic (Matching Concepts) Matching Results
  • 51. CONCLUSION More significant number of Tests To Draw more General Conclusions With regard to Comparatives Syntactic Systems Versus Semantic Systems
  • 52. CONCLUSION More Recommendations Reference Reference and Norms Ontologie(s) Alignments To achieve a good quality manual matching
  • 53. THE END