A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with at least one predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram. Containment identification rules identify one or more containment relationships in the graphical diagram. Multiplicity identification rules identify multiplicity relationships in the graphical diagram. Advanced semantic rules identify visual elements that represent attributes and refine relationships to identify unique behavior.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-5. (canceled) 6. A computerized method for inferring graphical model semantics, comprising: receiving, in a processor from a user, a graphical diagram; identifying a plurality of elements in said graphical diagram, each element having one or more visual cues; assigning scores to a plurality of considered correlations between visual cue criterions and types of a modeling language which the received diagram follows; selecting from the plurality of considered correlations, a correlation which has a highest or minimal assigned score; and customizing a meta-modeling tool to operate with the visual cue criterions of the selected correlation. 7. The method of claim 6, wherein the considered correlations include correlations for at least element shapes, element stereotypes and element colors. 8. The method of claim 6, wherein assigning scores to the considered correlations comprises assigning probabilities based on a set of graphical diagrams used for training. 9. The method of claim 8, wherein selecting a correlation comprises selecting a correlation with a highest probability. 10. The method of claim 6, wherein assigning scores to the considered correlations comprises constructing a tree for each considered correlation and assigning each tree a score indicative of a number of operations a user has to do to get to a full featured element in the diagram. 11. The method of claim 6, further comprising identifying in the graphical diagram elements contained in other elements and accordingly automatically defining rules regarding which types can be contained in which types. 12. A system for inferring graphical model semantics, comprising: a memory; and a processor configured to receive from a user, a graphical diagram, to identify a plurality of elements in said graphical diagram, each element having one or more visual cues, to assign scores to a plurality of considered correlations between visual cue criterions and types of a modeling language which the received diagram follows, to select from the plurality of considered correlations, a correlation which has a highest or minimal assigned score, and to customize a meta-modeling tool to operate with the visual cue criterions of the selected correlation. 13. The system of claim 12, wherein the considered correlations include correlations for at least element shapes, element stereotypes and element colors. 14. The system of claim 12, wherein the processor assigns probability scores to the correlations based on information from a set of graphical diagrams used for training. 15. The system of claim 14, wherein the processor is configured to select the correlation with a highest probability. 16. The system of claim 12, wherein the processor is configured to construct a tree for each considered correlation and to assign each tree a score indicative of a number of operations a user has to do to get to a full featured element in the diagram. 17. The system of claim 12, wherein the processor is further configured to identify in the graphical diagram elements contained in other elements and accordingly to automatically define rules regarding which types can be contained in which types.