1. A method for merging information of high semantic level representing complex situations comprising several items of information or data originating from several sensors, said method being executed on a processor and comprising: acquiring the several items of information or data arising from at least two sensors in raw form and transforming the several items of information or data using said processor to cast the several items of information or data into a form of conceptual graphs, a conceptual graph representing several concepts and relations which exist between the several concepts, a conceptual graph comprising several entity nodes and relation nodes, with a set of concept nodes E defined on a support S, two conceptual graphs G 1 and G 2 defined on the support S, and the two conceptual graphs G 1 and G 2 being stored in a database; defining a knowledge base containing information or data specific to a sector of an application of data merging and rules applied in said sector of the application, and transforming said information or data specific to the sector of the application using the processor to present said information or data specific to the sector of the application in a form of conceptual graphs; determining a merging strategy denoted strategy merge as follows: strategy merge =f merge ◯f comp :E×E→E∪{E×E}, where f merge :{true, false}×E×E→E∪{E×E} is a merge function for merging concept nodes of graphs, and f comp : E×E→{true, false}×E×E is a function for testing compatibility between two concept nodes of the graphs; applying the merging strategy to the information or data arising from the knowledge base and from an observation base taking the form of graphs to produce merged data; and sending the merged data to a decision making system, wherein: the two conceptual graphs G 1 and G 2 have a common generalization H, and projections P 1 :H→G 1 and P 2 :H→G 2 , P 1 and P 2 are compatible according to the function f comp if, for each concept c of the graph H, the following conditions are complied with: P 1 (c) and P 2 (c) have a common sub-type different from an absurd type, referents or components of P 1 (c) and P 2 (c) conform to their most general common sub-type, and the referents of P 1 (c) and P 2 (c) are either equal, or one of the two is undefined, or f comp (P 1 (c), P 2 (c))=(true, P 1 (c), P 2 (c)).
1. A method for merging information of high semantic level representing complex situations comprising several items of information or data originating from several sensors, said method being executed on a processor and comprising: acquiring the several items of information or data arising from at least two sensors in raw form and transforming the several items of information or data using said processor to cast the several items of information or data into a form of conceptual graphs, a conceptual graph representing several concepts and relations which exist between the several concepts, a conceptual graph comprising several entity nodes and relation nodes, with a set of concept nodes E defined on a support S, two conceptual graphs G 1 and G 2 defined on the support S, and the two conceptual graphs G 1 and G 2 being stored in a database; defining a knowledge base containing information or data specific to a sector of an application of data merging and rules applied in said sector of the application, and transforming said information or data specific to the sector of the application using the processor to present said information or data specific to the sector of the application in a form of conceptual graphs; determining a merging strategy denoted strategy merge as follows: strategy merge =f merge ◯f comp :E×E→E∪{E×E}, where f merge :{true, false}×E×E→E∪{E×E} is a merge function for merging concept nodes of graphs, and f comp : E×E→{true, false}×E×E is a function for testing compatibility between two concept nodes of the graphs; applying the merging strategy to the information or data arising from the knowledge base and from an observation base taking the form of graphs to produce merged data; and sending the merged data to a decision making system, wherein: the two conceptual graphs G 1 and G 2 have a common generalization H, and projections P 1 :H→G 1 and P 2 :H→G 2 , P 1 and P 2 are compatible according to the function f comp if, for each concept c of the graph H, the following conditions are complied with: P 1 (c) and P 2 (c) have a common sub-type different from an absurd type, referents or components of P 1 (c) and P 2 (c) conform to their most general common sub-type, and the referents of P 1 (c) and P 2 (c) are either equal, or one of the two is undefined, or f comp (P 1 (c), P 2 (c))=(true, P 1 (c), P 2 (c)). 4. The method according to claim 1 , wherein: compatibility of two elements is defined as a function of a proportion of constituents that are identical between the two elements; and a normalized similarity measure complies with the conditions: 
 ∀( e,e ′)ε E×E;sim ( e,e ′)= sim ( e′,e ), 
 ∀( e,e ′)ε E×E with e different from e, sim ( e,e )= sim ( e′,e ′)> sim ( e,e ′), and 
 ∀ eεEsim norm ( e,e )=1, where sim is a similarity index defined onE×E−>R+.