METHOD AND SYSTEM FOR IDENTIFYING GRAPHICAL MODEL SEMANTICS

• US 20100161524A1
• Filed: 12/19/2008
• Published: 06/24/2010
• Est. Priority Date: 12/19/2008
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Feldman, Zohar, Fisher, Amit, Amid, David, Anaby-Tavor, Ateret
• Application Number:
  US12/339,458
• Publication Number:
  US 20100161524A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

A system and method for identifying graphical model semantics, one aspect, receive a graphical diagram, associate each of a plurality of elements with one or more 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.

Claims

1. A computer-implemented method for identifying graphical model semantics, comprising:

receiving a graphical diagram;

associating each of a plurality of elements from the diagram with one or more predetermined meta-types;

identifying a plurality of types in the graphical diagram;

determining a category for each of elements in said graphical diagram;

executing containment relationship identification rules to identify one or more containers in the graphical diagram;

executing multiplicity identification rules to identify multiplicity relationships in the graphical diagram; and

executing advanced semantic rules to identify visual elements that represent attributes and refine relationships between the plurality of types to identify unique behavior.

2. The method of claim 1, wherein said predetermined meta-types describes one or more structures of model family associated with the plurality of elements.

3. The method of claim 1, wherein the step of executing multiplicity identification rules includes executing multiplicity identification rules for one or more of said plurality of types associated with a link meta-type.

4. The method of claim 1, wherein the graphical diagram includes a business model representing a business entity, a business issue, a business problem, a business process, a business strategy, or combinations thereof.

5. The method of claim 1, wherein the step of identifying a plurality of types includes using a probabilistic method.

6. The method of claim 5, wherein the probabilistic method includes a training phase and a runtime phase.

7. The method of claim 1, wherein the step of identifying a plurality of types includes using a compact representation method.

8. The method of claim 7, wherein the compact representation method is based on graph theory.

9. The method of claim 1, wherein the step of identifying a plurality of types includes using a probabilistic method, the probabilistic method including at least selecting one rule for type identification, said one rule being most inclusive rule when more than one rule identifies same set of types.

10. The method of claim 1, wherein said step of identifying a plurality of types includes:

choosing a probability model describing a type of probability distribution;

running a set of rules that identify types and collecting results from running the set of rules;

for each rule run in the running step, looking-up match probabilities value according to the probability model;

extracting a leading rule from said set of rules having highest matched probabilities value; and

executing the leading rule on the graphical diagram to obtain meta-model types.

11. The method of claim 1, wherein said step of identifying a plurality of types includes:

building a collection of trees, each tree corresponding to a specific order of criteria;

reducing average height of the trees;

reducing number of trees in the collection of trees by using one or more business rules;

selecting a candidate tree from said reduced collection of trees using a predetermined method; and

outputting one or more top level nodes of the candidate tree, the top level nodes corresponding to meta-model types.

12. The method of claim 11, wherein the step of selecting a candidate tree includes selecting a tree with minimum height.

13. The method of claim 11, wherein the step of selecting a candidate tree includes selecting a tree with minimum weight, wherein weights are determined according to number of elements from the graphical diagram that meet a predetermined criterion.

14. The method of claim 1, wherein the step of determining a category includes:

categorizing according to link types.

15. The method of claim 1, wherein the step of determining a category includes:

categorizing according to identified types.

16. The method of claim 1, further including:

performing a semantic representation stage to select said containment relationships, said multiplicity identification, and said advanced semantic rules.

17. A system for identifying graphical model semantics, comprising:

a processor;

a module operable to receive a graphical diagram, assign each of a plurality of elements in the graphical diagram with one or more predetermined meta-types, identify a plurality of types in the graphical diagram, and determine a category for each of elements in said graphical diagram; and

a rules execution module operable to identify one or more containment relationships in the graphical diagram, identify multiplicity relationships in the graphical diagram, and execute advanced semantic rules to identify visual elements that represent attributes and refine relationships between the plurality of types to identify unique behavior.

18. The system of claim 17, wherein said predetermined meta-types depend on model family.

19. The system of claim 17, wherein the rules execution module executes multiplicity identification rules for one or more of said plurality of types associated with a link meta-type.

20. The system of claim 17, wherein the graphical diagram includes a business model representing a business entity, a business issue, a business problem, a business process, a business strategy, or combinations thereof.

21. The system of claim 17, wherein the module is further operable to execute one or more type identifying rules on the graphical diagram and collect identified types from the step of executing said one or more type identifying rules.

22. The system of claim 21, wherein said module is further operable to use a supervised learning to extract a model family associated with the graphics diagram to derive said one or more predetermined meta-types.

23. The system of claim 17, wherein the module identifies a plurality of types using a probabilistic method, the probabilistic method including selecting one rule for type identification, said one rule being most inclusive rule when more than one rule identifies same set of types.

24. The system of claim 17, wherein the module identifies a plurality of types by building a collection of trees, each tree corresponding to a specific order of criteria;

reducing average height of the trees;

reducing number of trees in the collection of trees by using one or more business rules;

selecting a candidate tree from said reduced collection of trees using a predetermined method; and

outputting one or more top level nodes of the candidate tree, the top level nodes corresponding to meta-model types.

25. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method of identifying graphical model semantics, comprising:

receiving a graphical diagram;

associating each of a plurality of elements from the diagram with one or more predetermined meta-types;

identifying a plurality of types in the graphical diagram;

determining a category for each of elements in said graphical diagram;

executing containment identification rules to identify one or more containment relationships in the graphical diagram;

executing multiplicity identification rules to identify multiplicity relationships in the graphical diagram; and

executing advanced semantic rules to identify visual elements that represent attributes and refine relationships between the plurality of types to identify unique behavior.