![](bg7.png)
Table 3: Rankings of the experts.
Mapping Hamming
(sea, seas) 1 0.374 0.199
(forest, rain forests) 0.66 0.340 0.199
(Building, Mansions) 0.25 0.121 0.092
(forest, vidaucts) 0.08 0 0
Mapping Maedche
(sea, seas) 0.43 0 0
(forest, rain forests) 0.67 0.158 0.467
(Building, Mansions) 0.52 0 0.167
(forest, vidaucts) 0.55 0 0.167
Mapping Wu & Palmer
(sea, seas) 1 0.467 0.333
(forest, rain forests) 0.53 0.292 0.303
(Building, Mansions) 0.88 0.383 0.333
(forest, vidaucts) 0.25 0 0
Mapping Scoring (eq) Scoring (sub)
(sea, seas) 0.84 0.53
(forest, rain forests) 0.79 0.97
(Building, Mansions) 0.51 0.58
(forest, vidaucts) 0 0.16
U(a) = 0.374∗ u
1
(Sim
Hamming
)
+ 0.158∗ u
2
(Sim
Maedche
)
+ +0.46∗ u
3
(Sim
WUP
)
whereas its score in the subsumption context is
given by the following formula:
U(a) = 0.199∗ u
′
1
(Sim
Hamming
)
+ 0.467∗ u
′
2(Sim
Maedche
)
+ 0.333∗ u
′
3(Sim
WUP
)
These formula can be nowused to rank all the can-
didate mappings coming from SatellitesSceneOntol-
ogy and FTT ontologies.
5 CONCLUSION
In this paper, after presenting a literature review of
the main similarity models used to map or align on-
tology entities, we propose a semi-automatic mapping
selection process in order to build a satellite images
ontology by reusing geographical object ontologies.
The main advantage of our work is that it needs lit-
tle human intervention to monitor the mapping pro-
cess. First experimentations show that our approach
is promising.
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