a framework for knowledge sharing and integrity checking for multi-perspective models
DESCRIPTION
A Framework for Knowledge Sharing and Integrity Checking for Multi-Perspective Models. Yun-Heh (Jessica) Chen-Burger Artificial Intelligence Application Institute June 2001. Multi-Perspective Models are Used . Related Work Zachman’s Framework UML Modelling Suite Ulrich Frank Group’s MEMO - PowerPoint PPT PresentationTRANSCRIPT
Jessica Chen-Burger
A Framework for Knowledge Sharing and Integrity Checking for
Multi-Perspective Models
Yun-Heh (Jessica) Chen-BurgerArtificial Intelligence Application Institute
June 2001
Jessica Chen-Burger
Multi-Perspective Models are Used
Related Work– Zachman’s Framework– UML Modelling Suite– Ulrich Frank Group’s MEMO– Air Operation Enterprise Models
MPM is inevitable ?
Jessica Chen-Burger
Problems for Multi-Perspective Modelling
Complexities within a single model and between models:
– Problems with understanding the model
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
Jessica Chen-Burger
Solution 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
Automatic V&V within a model and between models Semantic-linked communication between models
Jessica Chen-Burger
Approach:Using a Light-Weight Ontology
(LWO) for Communication
Hierarchical and typed structure
Multiple-parents allowed
Non-circular type specifications
Knowledge is shared through the underlying common ontology
Jessica Chen-Burger
Knowledge Sharing via Light Weight Ontology
Ontology
Model-1 Model-2 Model-3
Jessica Chen-Burger
Multi-model Communication Describe the same problem domain
Describe different aspects of the domain
Commonly shared knowledge between models
Similar modelling principles
Pair-wise model mapping is possible– i.e. domain-model and another model
Global model mapping to some extend
Jessica Chen-Burger
Example Mapping of Model Primitives in Different Modelling
Languages
Domain Model: the light-weight ontology BSDM: Business System Development Method RACD: Role Activity and Communication Diagram IDEF0 IDEF3
Jessica Chen-Burger
Domain-Model BSDM RACD IDEF0 IDEF3 Example Instances
Concrete Class Entity Data Control Process: action Plan, Guidelines
Concrete Class Entity Role Mechanism Process: action Personnel, Equipments
Concrete Class Entity Data Input Process: action Information
Concrete Class Entity Data Output Process: action Information
Concrete Class Process Process Function UOB Actions
Jessica Chen-Burger
Achieving Global Consistency (1)
Local Consistency– Local consistency within each model
Pair-wise Consistency– Pair-wise consistency with domain-model
Global Consistency– Global consistency
Jessica Chen-Burger
Achieving Global Consistency (2)
1. Achieve local consistency for all models2. 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
Jessica Chen-Burger
Example Rule (1)Consistent Representation of Information
Value2.Value1
M2)T2), (O2, Att), ue2,model((Valribute_in_object_att M1)T1), (O1, Att), ue1,model((Valribute_in_object_att
O2)(O1M2)T2),e((O2,object_typM1)T1),e((O1,object_typ
) T2 (T1 M2)2,itive_of(Tmodel_prim M1)1,itive_of(Tmodel_prim
Value2Value1,Att,O2,O1,M2,M1,T1,
Jessica Chen-Burger
Example Rule (2) Correct Specialisation of Concepts
M2) S2,t(O2,sub_concep S2 S1
M2)T2), e((S2,object_typS2
M1)O1, t(S1,sub_concep M1)),1T e((S1,object_typ
2O1O )2 M),2T ,2e((Oobject_typ
M1)T1), e((O1,object_typ2T1T
)2 M,2itive_of(Tmodel_prim M1)1,itive_of(Tmodel_prim
S1O2, O1, M2,T2, M1,T1,
Jessica Chen-Burger
Example Rule (3) Transferable Property of Full Equivalence
)2M,2O,2D(on_depends2D1D
M2)T2), e((D2,object_typ
)1M,1D,1O(on_depends)1M,)1T,1D(e(object_typ
2O1O )2 M,)2T ,2e((Oobject_typ
M1),T1) e((O1,object_typ T2 T1
M2)2,itive_of(Tmodel_prim M1)1,itive_of(Tmodel_prim
1D,1O,2M,2T,1M,1T
Jessica Chen-Burger
Illustration Example for Rule (3)
Jessica Chen-Burger
Summary Current work is not completed, and will be extended
and deepen in areas where appropriate;
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.
Jessica Chen-Burger
Overall Challenges Not all modelling methods are compatible
– Different level of abstraction, e.g. IDEF0 is decomposable, whereas BSDM is not
– Difficulties in mapping model primitives Not all relationships or constraints are identified
– Some inconsistencies may be over-looked when multi-updating is carried out
Difficult to get an accurate global picture The recursive mending process is exponential and human
expert’s judgement must be exercised, when this occurs
Jessica Chen-Burger
Future Work Enhance concept mapping, i.e. to map concepts that
are not “fully equivalent” but only partially equivalent Enhance level of knowledge sharing by providing
checking and conflict resolving advisory mechanism Extend current V+V facilities by including a larger
and more compete set of verification rules Establish formal mechanism (theory) to help ensure
the quality of built enterprise models Establish measurable criteria for evaluating the
quality of models Provide a basis for assisting the process knowledge
argumentation – which is the process of building Enterprise Models
Provide a basis for building workflow systems
Jessica Chen-Burger
End of Slides
Thank you for listening !
Jessica Chen-Burger
Advantages of using a Light-Weight Ontology
Intuitive: – Visual presentation– Hierarchical classification– Direct mapping to underlying formal
representation 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
Jessica Chen-Burger
Challenges - Problems in Constructing the Ontology
The scope of the ontology Level of abstraction that are captured in the
ontology What has to be said in each concept? Classification of concepts Naming the concepts that are suitable across
different models