Co-design for Conceptual Spaces: An Agile Design Methodology for m-Learning
An agile process for the creation of conceptual models from content descriptions
description
Transcript of An agile process for the creation of conceptual models from content descriptions
An agile processfor the creation of conceptual models
from content descriptions
Hans-Werner Sehring
Centre for Sustainable Content Logistics
TuTech Innovation GmbH / Hamburg University of Technology
Joint work with:
Sebastian Boßung Henner Carl Joachim W. Schmidt
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Outline
1.Conceptual Content Management
2.Asset expressions and schemata
3.The Asset Schema Inference Process
4.Straight-forward schema inference
5.Cluster-based schema inference
6.Process evaluation
7.Summary and outlook
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1. Conceptual Content Management
Conceptual Content Management (CCM)– an approach to domain modelling– inspired by epistemology:
entity description by classes and instances, called Assets– Assets are dual entity descriptions consisting of
content visualising it and a conceptual model describing it– model-based system generation
Features:– modelling is carried out by domain experts– domain models are open to changes– existing work is preserved, even if changes are applied– communication between domain experts with individual
models is maintained
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CCM dynamics
CCM systems (CCMSs) are dynamically generated from domain models:
– immediately realizing model changes– preserving existing Assets– maintaining communication
Key contributions to this end:– modelling language– model compiler– architecture for
evolvable systems
model Historiographyfrom Time import Timestampfrom Topology import Placeclass Professor { content image concept characteristic n :String relationship publs :Work* }
Intermediate model(parse tree)
… … … …
a:AssetClass b:AssetClass
m:ModelsuperClass
Political_Iconography (PI)
ArtistsRegents
mclient1
client ( Regents )
mclient
client ( PI )
mmed2
mediation ( Regents , Artists )
DB
(Regents )
mclient2
client ( Artists )
DB
(Artists )
mmed1
mediation ( PI , ( Regents , Artists ))
mdistrib1
distribution ( PI , Regents )
mdistrib2
distribution ( PI , Artists )
DB
(PI )
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Model-driven development
All SW development starts with a conceptual model– especially model-driven development approaches call for
models with a sufficient degree of formality– CCM is similar to model-driven development in the respect
that software creation is highly automated– in CCM, software generation is even dynamic
A CCM model is required as a starting point for CCMSs– usually, some modelling expert (analyst) is consulted– due to dynamics requirement, such a modelling expert
cannot be employed in CCM– domain experts are not modelling experts; usually have
problems with, e.g., sufficient formality– but: experts can “tell their story” by providing examples
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2. Asset expressions and schemata
In many domains research starts by regarding instances (samples), not concepts
concerns: Teacher
name : Name
Ludwig Heydenreich
issued : Place
issuedWhen : Timestamp
issuedBy : Professor
name : NameGeorg Thilenius
: CareerStep
: Professorpublications: Work*
: Dissertation
title: Name
Die Sakralbau-Studien Leonardo da Vinci' s
reviewer: Professor
: Professorname : Name
Erwin Panofsky
: Book
title: Name
Architecturein Italy
: City
: FullProfessor
24 Feb 1934
where : GeoPoint
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Asset model from the example
Manually defined classes for the example:model Historiographyfrom Time import Timestampfrom Topology import Placeclass Professor { content image concept characteristic name :String relationship publications :Work* }class Work { content scan concept characteristic title :String relationship concerns :Professor* relationship issued :Issuing relationship reviewers :Professor*}class Issuing { concept relationship issued :Place relationship issuedBy :Professor relationship issuedWhen :Timestamp }
Models consisting of classesClasses with
• content handles and• attributes (and constraints)
• characteristics• relationships
Models consisting of classesClasses with
• content handles and• attributes (and constraints)
• characteristics• relationships
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Asset model from the example (cont’d)
Example of personalisation: a domain expert introduces the distinction of documents:model MyHistoriographyfrom Historiography import Work, Professorclass Work { concept relationship reviewer unused}class Dissertation refines Work { concept relationship reviewer :Professor*}
Import and redefinition of classes for• schema evolution (user communities)• personalisation (single users)• …
Import and redefinition of classes for• schema evolution (user communities)• personalisation (single users)• …
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3. Asset Schema Inference Process (ASIP)
Bootstrapping: CCM itself requires an initial model as a starting point for the open dynamic modelling process
Required: sytematic support for domain experts in finding suitable models
Start with Asset Expressions:– content abstractions and applications:
assigned names and bound values– semantic types (concepts): no inner structure
Concepts and classes are not distinguished in CCM models, intensional and extensional definitions
Free-form entity descriptions are used as samples; later they become instances of classes
reviewer: Professor
: Professor
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Agile CCMS development
Agility:– based on the possibility to generate CCMSs dynamically– domain experts review their models based on experiences
with an operational CCMS– if changes to the model are required, another iteration of
the process is started– entity descriptions created within the CCMS can be used as
samples for the next iteration of the process
Create Asset expressions
Construct schema
Generate CCMS
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ASIP phases
The ASIP has four phases
Sample acquisition
Schema inference
Feedback questions
Prototype generation
System generation
unhappy with schema:-modify samples(- modify schema)
answer questions
Phase 1
Phase 2
Phase 3
Phase 4
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Two schema inference experiments
Experiments with alternatives for phases 2 and 3:– (traditional) schema inference plus user feedback
straight-forward approach starting from singletons– clustering, supervised by domain experts
statistical approach, semi-supervised learning
Phase 3 (generation of questions to gather feedback) is determined by the alternative chosen
Result of phases 1-3 is a CCM model:– prototype generation and system generation (phase 4)
are carried out by the CCM model compiler– the domain expert can modify the inferred schema
(openness and dynamics)
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4. Straight-forward schema inference
Schema construction by traditional schema inference1. derive naive classes directly from the set of samples
2. apply simplifications
3. if changes where applied to the schema, repeat step 2
Step 1: for each sample create an Asset class with– a content handle whose type is determined by the encoding
format of the sample’s content– attributes for all abstractions over the content
• characteristics for certain known types• relationships for other types• no further constraints
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Schema simplification
Step 2: simplifications, repeatedly applied in the specified order
– identical class: unify classes with attributes and content handles with identical names and types
– inheritance: subtype relationship of classes whose sets of attributes are in a subset relationship
– type match: if two classes have attributes and content handles of identical types, prompt expert for unification
– inheritance orphan: ask domain expert about removal of classes with only few instances
Note:– often classes considered equal if the attributes’ types match– here the name is considered, or else feedback is collected
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5. Cluster-based schema inference
Schema construction by clustering:– cluster samples, create classes from clusters– experiment based on k-means algorithm
Clustering steps:– classification: assign classes to clusters based on distance
measure d:d(s,c) = α dsem(s,c) + (1-α) dstruct(s,c), α[0..1]
– optimisation: recompute the cluster centres– inheritance hierarchy creation: like in the simple approach– feedback: visualise the clusters, allow to partition clusters
=> semi-supervised learning
Less user interaction than in the traditional approach
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Structural distance measure
dstruct is based on the length of the shortest edit script (similar to string matching)
Costs like:edit operation cost magnitudeadd attribute lowremove attribute highchange attribute name lowbroaden attribute type mediumnarrow attribute type very lowincrease cardinality of attribute value mediumdecrease cardinality of attribute value very low
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Semantic distance measure
dsem is determined by the shortest paths in the class hierarchy
1/2h(T1) if T1 is direct supertype of TC
dsem(T1,Tm) + dsem(Tm,TC) if T1 is direct supertype of Tm
dsem(s,c) = and Tm is supertype of TC
dsem(TS,T1) + dsem(TS,TC) if TS is the most specific commonsupertype of T1 and TC
Any
WorkOfArt Person
Text Image
Bookh(T)
Any
WorkOfArt
Text
Book
Image
Person1/2 1/2
1/4
1
0
1
2
3distance?
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6. Process evaluation
Schema quality:– generally difficult to judge– for domain modelling: not a schema that describes sample
best, but model that best represents the application domain
Criteria [Cherfi, Akoka, Comyn-Wattiau]:– specification:
• graphical legibility• simplicity• expressiveness• syntactical correctness• semantic correctness
– usage: completeness, understandability– implementation: implementability, maintainability
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Process evaluation (cont’d)
Selected parameters:– simplicity: in general depends on
• the given sample set• domain expert’s answers in feedback phase
– syntactical correctness: granted by model generation– semantic correctness: can be negatively impacted by
structurally coinciding classes with different meanings– understandability:
• generated class names can be an obstacle• but: generated system lowers impact of schema
– implementability: by generation– maintainability: through dynamics
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7. Summary and outlook
Summary:– Conceptual Content Management allows domain experts
to provide and individually change domain models– domain experts are usually no modelling experts, and they
prefer to start with samples describing observations– a process helps domain experts defining initial models to
start the open dynamic CCM activity– as one novel approach a cluster-based schema inference
process has been investigated
Outlook: future work will include …– the inclusion of the cluster-based approach into the open
modelling for extensional concept definitions– the employment of reasoning techniques (induction,
abduction) to guide the schema construction process