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

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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 ?

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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

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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

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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

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Knowledge Sharing via Light Weight Ontology

Ontology

Model-1 Model-2 Model-3

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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

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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

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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

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Achieving Global Consistency (1)

Local Consistency

– Local consistency within each model

Pair-wise Consistency

– Pair-wise consistency with domain-model

Global Consistency

– Global consistency

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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

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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)(O1

M2)T2),e((O2,object_typ

M1)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,

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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_typ

2T1T

)2 M,2itive_of(Tmodel_prim

M1)1,itive_of(Tmodel_prim

S1O2, O1, M2,T2, M1,T1,

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Example Rule (3) Transferable Property of Full Equivalence

)2M,2O,2D(on_depends

2D1D

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

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Illustration Example for Rule (3)

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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.

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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

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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

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End of Slides

Thank you for listening !

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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

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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