1. Grupo de Procesado de Datos y Simulación
ETSI de Telecomunicación
Universidad Politécnica de Madrid
Towards Integrating Fuzzy Logic Capabilities into an
Ontology-based Inductive Logic Programming Framework
ISDA 2011
Josué Iglesias, Jens Lehmann
josue@grpss.ssr.upm.es

2. contents
 DL-based Inductive Logic Programming
 DL-Learner overview
 extending DL-Learner with fuzzy support
 semantic ILP problem test case
 experiments & results
 conclusions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

3. contents
 DL-based Inductive Logic Programming
 DL-Learner overview
 extending DL-Learner with fuzzy support
 semantic ILP problem test case
 experiments & results
 conclusions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

4. DL-based Inductive Logic Programming
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

5. DL-based Inductive Logic Programming
OWL-DL ontology
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

6. DL-based Inductive Logic Programming
OWL-DL ontology DL Knowledge Base
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

7. DL-based Inductive Logic Programming
OWL-DL ontology DL Knowledge Base +/- examples
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

8. DL-based Inductive Logic Programming
OWL-DL ontology DL Knowledge Base +/- examples
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

9. DL-based Inductive Logic Programming
OWL-DL ontology DL Knowledge Base +/- examples
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

10. DL-based Inductive Logic Programming
OWL-DL ontology DL Knowledge Base +/- examples
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

11. DL-based Inductive Logic Programming
OWL-DL ontology DL Knowledge Base +/- examples
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 1 / 10

12. contents

 DL-based Inductive Logic Programming
 DL-Learner overview
 extending DL-Learner with fuzzy support
 semantic ILP problem test case
 experiments & results
 conclusions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10

13. DL-Learner overview
+ +/-
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10

14. DL-Learner overview
+ +/-
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 2 / 10

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. 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. 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. contents

 DL-based Inductive Logic Programming

 DL-Learner overview
 extending DL-Learner with fuzzy support
 semantic ILP problem test case
 experiments & results
 conclusions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

19. extending DL-Learner with fuzzy support
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

20. extending DL-Learner with fuzzy support
fuzzy knowledge source component
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

21. extending DL-Learner with fuzzy support
fuzzy knowledge source component
objective
• classical (crisp) ontologies/KB  fuzzy ontologies/KB support
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

22. extending DL-Learner with fuzzy support
fuzzy knowledge source component
objective
• classical (crisp) ontologies/KB  fuzzy ontologies/KB support
candidates
• meta-ontologies
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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 extensions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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 tools
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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 tools
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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 tools
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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 approach
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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
 OWLAPI
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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
 OWLAPI
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 3 / 10

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. 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. extending DL-Learner with fuzzy support
fuzzy reasoning service component
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

33. extending DL-Learner with fuzzy support
fuzzy reasoning service component
objective
• support fuzzy ontology/KB reasoning
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

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. 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
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

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
• availability
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

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
• availability
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

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. 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. 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
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

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 integration
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

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 syntax
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 4 / 10

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. 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. extending DL-Learner with fuzzy support
fuzzy learning problem component
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

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. extending DL-Learner with fuzzy support
fuzzy learning problem component
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

48. extending DL-Learner with fuzzy support
fuzzy learning problem component
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

49. extending DL-Learner with fuzzy support
fuzzy learning problem component
fuzzy F-measure
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

50. extending DL-Learner with fuzzy support
fuzzy learning problem component
fuzzy F-measure
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

51. extending DL-Learner with fuzzy support
fuzzy learning problem component
fuzzy F-measure
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

52. extending DL-Learner with fuzzy support
fuzzy learning problem component
fuzzy F-measure
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

53. extending DL-Learner with fuzzy support
fuzzy learning problem component
fuzzy F-measure
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

54. extending DL-Learner with fuzzy support
fuzzy learning problem component
solutions
#1 [100%]
#2 [99.43%]
...
#N [83.12%]
fuzzy F-measure
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 5 / 10

55. extending DL-Learner with fuzzy support
fuzzy learning algorithm component
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 6 / 10

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. contents

 DL-based Inductive Logic Programming

 DL-Learner overview

 extending DL-Learner with fuzzy support
 semantic ILP problem test case
 experiments & results
 conclusions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 7 / 10

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. 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. 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. semantic ILP problem test case
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10

62. semantic ILP problem test case
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10

63. semantic ILP problem test case
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10

64. semantic ILP problem test case
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10

65. semantic ILP problem test case
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10

66. semantic ILP problem test case
fuzzy class definition
fuzzy concept assertion
fuzzy role assertion
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 8 / 10

67. contents

 DL-based Inductive Logic Programming

 DL-Learner overview

 extending DL-Learner with fuzzy support

 semantic ILP problem test case
 experiments & results
 conclusions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

68. experiments & results
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

69. experiments & results
crisp trains KB
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

70. experiments & results
OWL2
FuzzyOWL2
crisp trains KB
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

71. experiments & results
OWL2
FuzzyOWL2 + fuzzy
crisp trains KB
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

72. experiments & results
OWL2
=? 
FuzzyOWL2 + fuzzy
crisp trains KB
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

73. experiments & results
OWL2
=? 
FuzzyOWL2 + fuzzy
crisp trains KB
FuzzyOWL2 + fuzzy
fuzzy trains KB
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

74. experiments & results
OWL2
=? 
FuzzyOWL2 + fuzzy
crisp trains KB
FuzzyOWL2 + fuzzy

fuzzy trains KB
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

75. experiments & results
OWL2
=? 
FuzzyOWL2 + fuzzy
crisp trains KB
FuzzyOWL2 + fuzzy

fuzzy trains KB
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 9 / 10

76. contents

 DL-based Inductive Logic Programming

 DL-Learner overview

 extending DL-Learner with fuzzy support

 semantic ILP problem test case

 experiments & results
 conclusions
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10

77. conclusions
• DL-Learner + fuzzy support
 general purpose ILP tool
 OWL-based
 open-source
 http://dl-learner.org
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10

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 reasoner
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10

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. 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. any question?
International Conference on Intelligent Systems Design and Applications josue@grpss.ssr.upm.es 10 / 10