6. A system making it possible to manage named entities that are presented in different graphical or syntactic forms and refer to the same entity, contained in a database in which the system is capable of executing different physical actions at the database level to optimize the quality of the database and consequently obtain a better management thereof, wherein the system comprises at least the following elements: a processor and a memory for storing instructions which cause the processor to execute the following steps: apply a Levenshtein method to entities E i in the database in order to create a similarity between said entities, defining the measurement of similarity between two entities in the following manner S l ⁡ ( Ei , E j ) = 1 - D ⁡ ( E i , E j ) max ⁡ (  E i  ,  E j  ) with S i being the measurement of similarity between the two entities (E i , E j ) in the database, D being the Levenshtein distance between the two entities, E i respectively E j being the number of characters of E i , respectively E j , use the n-grams method to create a similarity S n (E i ,E j ) between the A entities constituting the database, by extracting from the two entities E i and E j the sets N i of the n-grams of E i and the set N j of the n-grams of E j , then defining the number of n-grams |N i | deduced from E i and the number of n-grams |N j | deduced from E j , the similarity between E 1 and E j is then defined by: S n ⁡ ( E i , E j ) =  N i ⋂ N j  min ⁡ (  N i  ,  N j  ) , use a method of merging the data on said entities in the database in order to construct at least 2 classifications, respectively relating to the n-grams similarity with n greater than or equal to 2, said classifications corresponding to sets of entity partitions P i , wherein the partitions Pi correspond with the classifications of all the entities in the database, and find the consensus partition P c between the M partitions found, using the canonic form entity E c to view the result or else modify the entities present in the database through the canonic form entity, an unstructured database ( 3 ) which can contain texts, a buffer memory ( 4 ) making it possible to store all the data for which the matching is required.
6. A system making it possible to manage named entities that are presented in different graphical or syntactic forms and refer to the same entity, contained in a database in which the system is capable of executing different physical actions at the database level to optimize the quality of the database and consequently obtain a better management thereof, wherein the system comprises at least the following elements: a processor and a memory for storing instructions which cause the processor to execute the following steps: apply a Levenshtein method to entities E i in the database in order to create a similarity between said entities, defining the measurement of similarity between two entities in the following manner S l ⁡ ( Ei , E j ) = 1 - D ⁡ ( E i , E j ) max ⁡ (  E i  ,  E j  ) with S i being the measurement of similarity between the two entities (E i , E j ) in the database, D being the Levenshtein distance between the two entities, E i respectively E j being the number of characters of E i , respectively E j , use the n-grams method to create a similarity S n (E i ,E j ) between the A entities constituting the database, by extracting from the two entities E i and E j the sets N i of the n-grams of E i and the set N j of the n-grams of E j , then defining the number of n-grams |N i | deduced from E i and the number of n-grams |N j | deduced from E j , the similarity between E 1 and E j is then defined by: S n ⁡ ( E i , E j ) =  N i ⋂ N j  min ⁡ (  N i  ,  N j  ) , use a method of merging the data on said entities in the database in order to construct at least 2 classifications, respectively relating to the n-grams similarity with n greater than or equal to 2, said classifications corresponding to sets of entity partitions P i , wherein the partitions Pi correspond with the classifications of all the entities in the database, and find the consensus partition P c between the M partitions found, using the canonic form entity E c to view the result or else modify the entities present in the database through the canonic form entity, an unstructured database ( 3 ) which can contain texts, a buffer memory ( 4 ) making it possible to store all the data for which the matching is required. 7. The system according to claim 6 , wherein the system comprises a device ( 23 ) for taking decisions following a request ( 20 ) formulated by a user.