1. Jessica Chen-Burger A Framework for Knowledge Sharing and Integrity Checking for Multi-Perspective Models Yun-Heh (Jessica) Chen-Burger Artificial Intelligence Application Institute June 2001

2. Jessica Chen-Burger Multi-Perspective Models are Used l Related Work –Zachman’s Framework –UML Modelling Suite –Ulrich Frank Group’s MEMO –Air Operation Enterprise Models l MPM is inevitable ?

3. Jessica Chen-Burger Problems for Multi-Perspective Modelling l Complexities within a single model and between models: –Problems with understanding the model l Inconsistencies within a single model and between models: –Problems with obtaining the correctness and consistency for all models –Problems with managing and reflecting (frequent) changes in the described domain

4. Jessica Chen-Burger Solution l User friendly interface: –Multi-view user interfaces (intelligent or not) –Semantic-linked traversing and browsing –Simulation, animation and abstraction of dynamic behaviours –Tutoring in model construction l Automatic V&V within a model and between models l Semantic-linked communication between models

5. Jessica Chen-Burger Approach: Using a Light-Weight Ontology (LWO) for Communication l Hierarchical and typed structure l Multiple-parents allowed l Non-circular type specifications l Knowledge is shared through the underlying common ontology

6. Jessica Chen-Burger Knowledge Sharing via Light Weight Ontology Ontology Model-1Model-2 Model-3

7. Jessica Chen-Burger Multi-model Communication l Describe the same problem domain l Describe different aspects of the domain l Commonly shared knowledge between models l Similar modelling principles l Pair-wise model mapping is possible –i.e. domain-model and another model l Global model mapping to some extend

8. Jessica Chen-Burger Example Mapping of Model Primitives in Different Modelling Languages l Domain Model: the light-weight ontology l BSDM: Business System Development Method l RACD: Role Activity and Communication Diagram l IDEF0 l IDEF3

9. Jessica Chen-Burger Domain-ModelBSDMRACDIDEF0IDEF3Example Instances Concrete ClassEntityDataControlProcess: actionPlan, Guidelines Concrete ClassEntityRoleMechanismProcess: actionPersonnel, Equipments Concrete ClassEntityDataInputProcess: actionInformation Concrete ClassEntityDataOutputProcess: actionInformation Concrete ClassProcess FunctionUOBActions

10. Jessica Chen-Burger Achieving Global Consistency (1) l Local Consistency –Local consistency within each model l Pair-wise Consistency –Pair-wise consistency with domain-model l Global Consistency –Global consistency

11. Jessica Chen-Burger Achieving Global Consistency (2) 1. Achieve local consistency for all models 2. Select one model to achieve a pair-wise consistency with the domain-model to form an initial consistent set –Knowledge transfer to Domain-Model –For each discrepancy, do recursive and dependency- directed modification and convergence 3. Add a new model to the consistent set –Knowledge transfer to Domain-Model –For each discrepancy, do recursive and dependency- directed mending in the previous models and the new model 4. Repeat step 3 until all models are consistent with each other

12. Jessica Chen-Burger Example Rule (1) Consistent Representation of Information

13. Jessica Chen-Burger Example Rule (2) Correct Specialisation of Concepts

14. Jessica Chen-Burger Example Rule (3) Transferable Property of Full Equivalence

15. Jessica Chen-Burger Illustration Example for Rule (3)

16. Jessica Chen-Burger Summary l Current work is not completed, and will be extended and deepen in areas where appropriate; l A more rigorous approach may be established and adapted for measuring the various qualities of the rules and models using them in several aspects, e.g. –Which types of models are most suitable for those checking rules; –To which extent can such rules ensure the quality of the checked models.

17. Jessica Chen-Burger Overall Challenges l Not all modelling methods are compatible –Different level of abstraction, e.g. IDEF0 is decomposable, whereas BSDM is not –Difficulties in mapping model primitives l Not all relationships or constraints are identified –Some inconsistencies may be over-looked when multi- updating is carried out l Difficult to get an accurate global picture l The recursive mending process is exponential and human expert’s judgement must be exercised, when this occurs

18. Jessica Chen-Burger Future Work l Enhance concept mapping, i.e. to map concepts that are not “fully equivalent” but only partially equivalent l Enhance level of knowledge sharing by providing checking and conflict resolving advisory mechanism l Extend current V+V facilities by including a larger and more compete set of verification rules l Establish formal mechanism (theory) to help ensure the quality of built enterprise models l Establish measurable criteria for evaluating the quality of models l Provide a basis for assisting the process knowledge argumentation – which is the process of building Enterprise Models l Provide a basis for building workflow systems

19. Jessica Chen-Burger End of Slides Thank you for listening !

20. Jessica Chen-Burger Advantages of using a Light- Weight Ontology l Intuitive: –Visual presentation –Hierarchical classification –Direct mapping to underlying formal representation l Concise, precise and rich in semantic: –Provides a common languages among models –Can provide a basis for semantic-related translation, integration and communication automation –Automatic V&V between models –Semantic-Linked traversing and browsing

21. Jessica Chen-Burger Challenges - Problems in Constructing the Ontology l The scope of the ontology l Level of abstraction that are captured in the ontology l What has to be said in each concept? l Classification of concepts l Naming the concepts that are suitable across different models