Grupo de Procesado de Datos y Simulación                                                ETSI de Telecomunicación          ...
contents                   DL-based Inductive Logic Programming                   DL-Learner overview                  ...
contents                   DL-based Inductive Logic Programming                   DL-Learner overview                  ...
DL-based Inductive Logic ProgrammingInternational Conference on Intelligent Systems Design and Applications   josue@grpss....
DL-based Inductive Logic Programming                 OWL-DL ontologyInternational Conference on Intelligent Systems Design...
DL-based Inductive Logic Programming                 OWL-DL ontology                             DL Knowledge BaseInternat...
DL-based Inductive Logic Programming                 OWL-DL ontology                             DL Knowledge Base        ...
DL-based Inductive Logic Programming                 OWL-DL ontology                             DL Knowledge Base        ...
DL-based Inductive Logic Programming                 OWL-DL ontology                             DL Knowledge Base        ...
DL-based Inductive Logic Programming                 OWL-DL ontology                             DL Knowledge Base        ...
DL-based Inductive Logic Programming                 OWL-DL ontology                             DL Knowledge Base        ...
contents                             DL-based Inductive Logic Programming                   DL-Learner overview        ...
DL-Learner overview                                           +                              +/-International Conference o...
DL-Learner overview                                           +                              +/-International Conference o...
DL-Learner overview                                           +                              +/-                          ...
DL-Learner overview                                           +                              +/-                          ...
DL-Learner overview                                           +                              +/-                          ...
contents                             DL-based Inductive Logic Programming                             DL-Learner overv...
extending DL-Learner with fuzzy supportInternational Conference on Intelligent Systems Design and Applications   josue@grp...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source componentInternational Conf...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy knowledge source component                  ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service componentInternational Con...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy reasoning service component                 ...
extending DL-Learner with fuzzy support                                 fuzzy learning problem componentInternational Conf...
extending DL-Learner with fuzzy support                                 fuzzy learning problem component                  ...
extending DL-Learner with fuzzy support                                 fuzzy learning problem componentInternational Conf...
extending DL-Learner with fuzzy support                                 fuzzy learning problem componentInternational Conf...
extending DL-Learner with fuzzy support                                 fuzzy learning problem component  fuzzy F-measureI...
extending DL-Learner with fuzzy support                                 fuzzy learning problem component  fuzzy F-measureI...
extending DL-Learner with fuzzy support                                 fuzzy learning problem component  fuzzy F-measureI...
extending DL-Learner with fuzzy support                                 fuzzy learning problem component  fuzzy F-measureI...
extending DL-Learner with fuzzy support                                 fuzzy learning problem component  fuzzy F-measureI...
extending DL-Learner with fuzzy support                                 fuzzy learning problem component                  ...
extending DL-Learner with fuzzy support                                 fuzzy learning algorithm componentInternational Co...
extending DL-Learner with fuzzy support                                 fuzzy learning algorithm component                ...
contents                             DL-based Inductive Logic Programming                             DL-Learner overv...
semantic ILP problem test case   Michalski’s train ILP problem [16]    [16] J. Larson and R. S. Michalski, “Inductive infe...
semantic ILP problem test case   Michalski’s train ILP problem [16]                      Konstantopoulos–Charalambidis’   ...
semantic ILP problem test case   Michalski’s train ILP problem [16]                      Konstantopoulos–Charalambidis’   ...
semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications   josue@grpss.ssr.up...
semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications   josue@grpss.ssr.up...
semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications   josue@grpss.ssr.up...
semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications   josue@grpss.ssr.up...
semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications   josue@grpss.ssr.up...
semantic ILP problem test case                                                                                    fuzzy cl...
contents                             DL-based Inductive Logic Programming                             DL-Learner overv...
experiments & resultsInternational Conference on Intelligent Systems Design and Applications   josue@grpss.ssr.upm.es   9 ...
experiments & results  crisp trains KBInternational Conference on Intelligent Systems Design and Applications   josue@grps...
experiments & results                                     OWL2                                  FuzzyOWL2  crisp trains KB...
experiments & results                                     OWL2                                  FuzzyOWL2                 ...
experiments & results                                     OWL2                                                            ...
experiments & results                                     OWL2                                                            ...
experiments & results                                     OWL2                                                            ...
experiments & results                                     OWL2                                                            ...
contents                             DL-based Inductive Logic Programming                             DL-Learner overv...
conclusions        • DL-Learner + fuzzy support                 general purpose ILP tool                 OWL-based      ...
conclusions        • DL-Learner + fuzzy support                 general purpose ILP tool                 OWL-based      ...
conclusions        • DL-Learner + fuzzy support                 general purpose ILP tool                 OWL-based      ...
conclusions        • DL-Learner + fuzzy support                 general purpose ILP tool                 OWL-based      ...
any question?International Conference on Intelligent Systems Design and Applications    josue@grpss.ssr.upm.es   10 / 10
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[ISDA'11] Towards integrating fuzzy logic capabilities into an ontology based inductive logic programming framework

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[ISDA'11] Towards integrating fuzzy logic capabilities into an ontology based inductive logic programming framework

  1. 1. Grupo de Procesado de Datos y Simulación ETSI de Telecomunicación Universidad Politécnica de Madrid Towards Integrating Fuzzy Logic Capabilities into anOntology-based Inductive Logic Programming Framework ISDA 2011 Josué Iglesias, Jens Lehmann josue@grpss.ssr.upm.es
  2. 2. contents  DL-based Inductive Logic Programming  DL-Learner overview  extending DL-Learner with fuzzy support  semantic ILP problem test case  experiments & results  conclusionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  3. 3. contents  DL-based Inductive Logic Programming  DL-Learner overview  extending DL-Learner with fuzzy support  semantic ILP problem test case  experiments & results  conclusionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  4. 4. DL-based Inductive Logic ProgrammingInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  5. 5. DL-based Inductive Logic Programming OWL-DL ontologyInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  6. 6. DL-based Inductive Logic Programming OWL-DL ontology DL Knowledge BaseInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  7. 7. DL-based Inductive Logic Programming OWL-DL ontology DL Knowledge Base +/- examplesInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  8. 8. DL-based Inductive Logic Programming OWL-DL ontology DL Knowledge Base +/- examplesInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  9. 9. DL-based Inductive Logic Programming OWL-DL ontology DL Knowledge Base +/- examplesInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  10. 10. DL-based Inductive Logic Programming OWL-DL ontology DL Knowledge Base +/- examplesInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  11. 11. DL-based Inductive Logic Programming OWL-DL ontology DL Knowledge Base +/- examplesInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10
  12. 12. contents   DL-based Inductive Logic Programming  DL-Learner overview  extending DL-Learner with fuzzy support  semantic ILP problem test case  experiments & results  conclusionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10
  13. 13. DL-Learner overview + +/-International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10
  14. 14. DL-Learner overview + +/-International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10
  15. 15. DL-Learner overview + +/- solutions #1 [100%] #2 [99.43%] ... #N [83.12%]International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10
  16. 16. DL-Learner overview + +/- solutions #1 [100%] #2 [99.43%] ... #N [83.12%]International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10
  17. 17. DL-Learner overview + +/- fuzzy support?? solutions #1 [100%] #2 [99.43%] ... #N [83.12%]International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10
  18. 18. contents   DL-based Inductive Logic Programming   DL-Learner overview  extending DL-Learner with fuzzy support  semantic ILP problem test case  experiments & results  conclusionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  19. 19. extending DL-Learner with fuzzy supportInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  20. 20. extending DL-Learner with fuzzy support fuzzy knowledge source componentInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  21. 21. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB supportInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  22. 22. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologiesInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  23. 23. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies • OWL recommendation extensionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  24. 24. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies • OWL recommendation extensions • OWL-based available toolsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  25. 25. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies  semantic-friendly  less human-understandable • OWL recommendation extensions • OWL-based available toolsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  26. 26. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies  semantic-friendly  less human-understandable • OWL recommendation extensions  solid semantics  no standardization expected • OWL-based available toolsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  27. 27. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies  semantic-friendly  less human-understandable • OWL recommendation extensions  solid semantics  no standardization expected • OWL-based available tools  standard & tools compatible  sematic-less approachInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  28. 28. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies  semantic-friendly  less human-understandable • OWL recommendation extensions  solid semantics  no standardization expected • OWL-based available tools  standard & tools compatible  sematic-less approach final implementation • FuzzyOWL2 (fuzzy OWL annotations)  active project  parser FuzzyOWL2  reasoners syntax  OWLAPIInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  29. 29. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies  semantic-friendly  less human-understandable • OWL recommendation extensions  solid semantics  no standardization expected • OWL-based available tools  standard & tools compatible  sematic-less approach final implementation • FuzzyOWL2 (fuzzy OWL annotations)  active project  parser FuzzyOWL2  reasoners syntax  OWLAPIInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  30. 30. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies  semantic-friendly  less human-understandable • OWL recommendation extensions  solid semantics  no standardization expected • OWL-based available tools  standard & tools compatible  sematic-less approach final implementation • FuzzyOWL2 (fuzzy OWL annotations) fuzzy classes definitions fuzzy concept assertions  active project fuzzy data types definitions  parser FuzzyOWL2  reasoners syntax fuzzy modifiers fuzzy role assertions  OWLAPI etc.International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  31. 31. extending DL-Learner with fuzzy support fuzzy knowledge source component objective • classical (crisp) ontologies/KB  fuzzy ontologies/KB support candidates • meta-ontologies  semantic-friendly  less human-understandable Protégé plug-in • OWL recommendation extensions  solid semantics  no standardization expected • OWL-based available tools  standard & tools compatible  sematic-less approach final implementation • FuzzyOWL2 (fuzzy OWL annotations) fuzzy classes definitions fuzzy concept assertions  active project fuzzy data types definitions  parser FuzzyOWL2  reasoners syntax fuzzy modifiers fuzzy role assertions  OWLAPI etc.International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10
  32. 32. extending DL-Learner with fuzzy support fuzzy reasoning service componentInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  33. 33. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoningInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  34. 34. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  35. 35. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDLInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  36. 36. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availabilityInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  37. 37. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availabilityInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  38. 38. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availability • Java-based interface (to ease DL-Learner integration)International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  39. 39. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availability • Java-based interface (to ease DL-Learner integration)International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  40. 40. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availability • Java-based interface (to ease DL-Learner integration) • FuzzyOWL2 integrationInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  41. 41. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availability • Java-based interface (to ease DL-Learner integration) • FuzzyOWL2 integrationInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  42. 42. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availability • Java-based interface (to ease DL-Learner integration) • FuzzyOWL2 integration final implementation • fuzzyDL  active project (most up-to-date development)  Java-based  parser FuzzyOWL2  fuzzyDL reasoner syntaxInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  43. 43. extending DL-Learner with fuzzy support fuzzy reasoning service component objective • support fuzzy ontology/KB reasoning candidates • few candidates  [based on reduction procedures (e.g., DeLorean)]  GERDS GURDL FiRE Yadlr fuzzyDL • availability • Java-based interface (to ease DL-Learner integration) • FuzzyOWL2 integration final implementation • fuzzyDL  active project (most up-to-date development) OWLAPI  Java-based encapsulation  parser FuzzyOWL2  fuzzyDL reasoner syntax (and extension)International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  44. 44. extending DL-Learner with fuzzy support fuzzy reasoning service component GERDS GURDL FiRE Yadlr fuzzyDL OWLAPI encapsulation (and extension)International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10
  45. 45. extending DL-Learner with fuzzy support fuzzy learning problem componentInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  46. 46. extending DL-Learner with fuzzy support fuzzy learning problem component objective • +/- examples International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  47. 47. extending DL-Learner with fuzzy support fuzzy learning problem componentInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  48. 48. extending DL-Learner with fuzzy support fuzzy learning problem componentInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  49. 49. extending DL-Learner with fuzzy support fuzzy learning problem component fuzzy F-measureInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  50. 50. extending DL-Learner with fuzzy support fuzzy learning problem component fuzzy F-measureInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  51. 51. extending DL-Learner with fuzzy support fuzzy learning problem component fuzzy F-measureInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  52. 52. extending DL-Learner with fuzzy support fuzzy learning problem component fuzzy F-measureInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  53. 53. extending DL-Learner with fuzzy support fuzzy learning problem component fuzzy F-measureInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  54. 54. extending DL-Learner with fuzzy support fuzzy learning problem component solutions #1 [100%] #2 [99.43%] ... #N [83.12%] fuzzy F-measureInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10
  55. 55. extending DL-Learner with fuzzy support fuzzy learning algorithm componentInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 6 / 10
  56. 56. extending DL-Learner with fuzzy support fuzzy learning algorithm component algorithm • candidate concepts generation • CELOE (Class Expression Learning for Ontology Engineering)  others already developed and ready to be used (refinement operator techniques) • no significant modifications  fuzzy reasoner OWLAPI encapsulation (and extension)International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 6 / 10
  57. 57. contents   DL-based Inductive Logic Programming   DL-Learner overview   extending DL-Learner with fuzzy support  semantic ILP problem test case  experiments & results  conclusionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 7 / 10
  58. 58. semantic ILP problem test case Michalski’s train ILP problem [16] [16] J. Larson and R. S. Michalski, “Inductive inference of VL decision rules,” SIGART Bull., pp. 38–44, June 1977.International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 7 / 10
  59. 59. semantic ILP problem test case Michalski’s train ILP problem [16] Konstantopoulos–Charalambidis’ fuzzy trains ILP problem [3] fuzzy definition fuzzy reasoning [3] S. Konstantopoulos and A. Charalambidis, “Formulating description logic learning as an inductive logic programming task,” in Proc. of FUZZ-IEEE, 2010 IEEE World Congress on Comp. Int., July 18–23, Barcelona. IEEE, Jul. 2010. [16] J. Larson and R. S. Michalski, “Inductive inference of VL decision rules,” SIGART Bull., pp. 38–44, June 1977.International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 7 / 10
  60. 60. semantic ILP problem test case Michalski’s train ILP problem [16] Konstantopoulos–Charalambidis’ fuzzy trains ILP problem [3] fuzzy definition OWL-based fuzzy reasoning generic tool [3] S. Konstantopoulos and A. Charalambidis, “Formulating description logic learning as an inductive logic programming task,” in Proc. of FUZZ-IEEE, 2010 IEEE World Congress on Comp. Int., July 18–23, Barcelona. IEEE, Jul. 2010. [16] J. Larson and R. S. Michalski, “Inductive inference of VL decision rules,” SIGART Bull., pp. 38–44, June 1977.International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 7 / 10
  61. 61. semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10
  62. 62. semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10
  63. 63. semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10
  64. 64. semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10
  65. 65. semantic ILP problem test caseInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10
  66. 66. semantic ILP problem test case fuzzy class definition fuzzy concept assertion fuzzy role assertionInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10
  67. 67. contents   DL-based Inductive Logic Programming   DL-Learner overview   extending DL-Learner with fuzzy support   semantic ILP problem test case  experiments & results  conclusionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  68. 68. experiments & resultsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  69. 69. experiments & results crisp trains KBInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  70. 70. experiments & results OWL2 FuzzyOWL2 crisp trains KBInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  71. 71. experiments & results OWL2 FuzzyOWL2 + fuzzy crisp trains KBInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  72. 72. experiments & results OWL2 =?  FuzzyOWL2 + fuzzy crisp trains KBInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  73. 73. experiments & results OWL2 =?  FuzzyOWL2 + fuzzy crisp trains KB FuzzyOWL2 + fuzzy fuzzy trains KBInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  74. 74. experiments & results OWL2 =?  FuzzyOWL2 + fuzzy crisp trains KB FuzzyOWL2 + fuzzy  fuzzy trains KBInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  75. 75. experiments & results OWL2 =?  FuzzyOWL2 + fuzzy crisp trains KB FuzzyOWL2 + fuzzy  fuzzy trains KBInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10
  76. 76. contents   DL-based Inductive Logic Programming   DL-Learner overview   extending DL-Learner with fuzzy support   semantic ILP problem test case   experiments & results  conclusionsInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10
  77. 77. conclusions • DL-Learner + fuzzy support  general purpose ILP tool  OWL-based  open-source  http://dl-learner.orgInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10
  78. 78. conclusions • DL-Learner + fuzzy support  general purpose ILP tool  OWL-based  open-source  http://dl-learner.org • real integration of up-to-date fuzzy ontologies tools  FuzzyOWL2: fuzzy ontology representation tool  fuzzyDL: fuzzy DL reasonerInternational Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10
  79. 79. conclusions • DL-Learner + fuzzy support  general purpose ILP tool  OWL-based  open-source  http://dl-learner.org • real integration of up-to-date fuzzy ontologies tools  FuzzyOWL2: fuzzy ontology representation tool  fuzzyDL: fuzzy DL reasoner • preliminar evaluation  functional correctness  response time ( OWLAPI encapsulation  new fuzzy reasoners)International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10
  80. 80. conclusions • DL-Learner + fuzzy support  general purpose ILP tool  OWL-based  open-source  http://dl-learner.org • real integration of up-to-date fuzzy ontologies tools  FuzzyOWL2: fuzzy ontology representation tool  fuzzyDL: fuzzy DL reasoner • preliminar evaluation  functional correctness  response time ( OWLAPI encapsulation  new fuzzy reasoners)  extend test case  more complex fuzzy trains example  real world problem (recommendation / shop assistant)International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10
  81. 81. any question?International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10

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