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.

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