The document discusses creating an integrated domain, task, and competency model. It provides an overview of semantic technologies for organizations and examples of conceptual models. It also discusses the APOSDLE meta-model and proposes a modeling activity to integrate domain, tasks, and learning goals.

1. Creating integrated domain, task
and competency model
Luciano Serafini
FBK­irst, Trento, Italy
Joint WORK with the partner of the
APOSDLE EU PROJECT

2. Overview
● Semantic technologies for organizations
● Examples of conceptual models
● The APOSDLE meta­model
● Basic facts about ontologies
● Proposal for a modeling activity

3. In complex organizations
● Too many applications are being built with
proprietary structures that are non­
interoperable
● Many are busy mapping across islands using
at best databases, and XML, but at worst
documents and spreadsheets
● Semantic technology is a key enabler for
realizing the renewed vision for integrating
systems in complex organizaiton

4. Semantic technologies enables...
● System interoperability
● Model­based systems engineering
● Organizational memory
● Knowledge management and reuse
● Learning in work space

5. Basic reference architecture
Application
Application
M1
M2
M4
M3
Application
Application

6. Short introduction to APOSDLE
learning platform

7. APOSDLE Key Distinctions:
Learning Perspective 19 May, 2009 / 7
 Integrated support for
 learner, knowledgeable person and worker
 learning activities within work and learning processes
 within computational work environment
 utilizing organizational memory
 Self­Directed (SD) Work­Integrated Learning (WIL)
Third Annual Review Meeting, Graz

8. 19 May, 2009 / 8
APOSDLE Key Distinctions:
Technological Perspective
● Hybrid Approach:
Coarse grained semantic models
complemented with soft computing
approaches
– Automatic discovery of work task/topic based on user interactions
– Automatic maintenance of user profiles based on user interactions
– Automatic identification of similarities based on text, multi­media data and
semantic analysis
– Automatic identification of prerequisite relations based on semantic analysis
Third Annual Review Meeting, Graz

9. APOSDLE P3
Alan Use Sara
UC Actor !
Cases?
Users RE
Process
UC ...
APOSDLE Working tools
Tools Learning tools
Collaboration tools
APOSDLE Integrated User Profiles Associative Network
Knowledge
Platform Structure Work Context
Skills
Semantic
Competency
Learning
Structures Domain­Model
Domain Model Process Model
Process Model Performance
(Ontology)
(Ontology) Goal Model
Structure
Organizational Backend
Systems
IT-Infrastructure
Database File­ LMS CMS ...
Server
Third Annual Review Meeting, Graz

10. Integrated Modeling of
Domain, Tasks and Learning
Goals
3rd Review Meeting, Graz

11. Short introduction on ontology
engineering

12. The goal of conceptual modeling
● To construct a conceptualization of a domain
that describes the aspects of a domain which
are relevant to a certain (set of) application.
● What is a Conceptualization? It is a formal
representation of a domain in terms of a set of
Concepts and a set of Relations between
concepts.

13. Example of Conceptualization
Conceptual Graphs

14. Example of Conceptualizatio
Topic maps

15. Example of conceptualization
Semantic networks

16. Example of conceptualization
RDF­gaphs

17. Example of conceptualization
Taxonomic classification

18. Example of conceptualization
Partinomy

19. Example of conceptualization
Web Ontology

20. Formal ontology
● A formal ontology is a special type of conceptualization
based on logic
● ( + ) Advantages of logic:
( + )
– It is an UNAMBIGUOUS language
– It is MACHINE UNDERSTANDABLE
– It is possible to implement AUTOMATIC
REASONING ALGORITHMS
● ( – ) Drawbacks of logic: it is NOT INTUITIVE for humans.
( – )
– Difficulties to read logic
– Difficulties to formalize concepts in logic

21. Try yourself:
Elephants are gray mammal which have a trunck
Elephant = Mammal ⊓ ∃ bodyPart.Trunk ⊓
∀color.Gray
Elephants are heavy mammals, except for Dumbo
elephants that are light
Elephant = Mammal ⊓
(∀weight.heavy ⊔ (Dumbo ⊓ ∀weight.Light)a

22. A four slide Introduction to
ontologies
(1)The language of ontologies
(2)It's meaning
(3)Expressing general knowledge (Tbox)
(4)Expressing specific knowledge (Abox)

23. Ontologies are formal theories
based on a formal language:
● Basic components of the language of
ontologies are
● CONCEPTS (aka CLASSES, TYPES)
– ANIMAL, FELINE, CAT, TAIL, ...
● RELATIONS (aka ROLES, ATTRIBUTES)
– LOVES, IS_FRIEND_OF, LIVES_IN
● INDIVIDUALS (aka OBJECTS, CONSTANTS)
– Garfield, John, Italy, France, ...

24. Formal languages has an
unambiguous interpretation
● Every concept A is interpreted in a set.
– CAT = (Fido, Garfield, Felix, cat1, cat2, ...}
– TAIL = {tail­of­fido, tail­of­garfield, … }
● The elements of a concept are called Instances of
the concepts
– Fido, Garfield, ... are instances of the concept CAT,
● Every relation R is interpreted in a set of pairsof
instances
– LOVES = {<john,mary> <paolo,elena>,
<luciano,cecilia> … }

25. Axioms are statements in the formal
language which holds on the
domain we want to describe (1)
● A Subclass of B means that all the instances
of A are also instances of B
– CAT SubClass of ANIMAL means that each cat is
also an animal
● R Subrelation of S = all the pairst in R are also
contained in S
– IS_FRIEND_OF SubRelation KNOWS means
that it's not possible for two individuals to be
friends without knowing eachother

26. Axioms are statements in the formal
language which holds on the
domain we want to describe (1)
● o ofType C (also written as C(o)) means that
the object o is contained in the set of instances
of C
– Italy ofType COUNTRY, means that
COUNTRY = {.... italy … }
● o R o' (also written as R(o,o)) means that the
object o is in relation R with the object o', I .e.
that R = {… <o,o'> …}
– Trentino is_pert_of Italy, means that trentino
region is a part of the italian territory.

27. Ontology engineering
● Ontology engineering is the “art” of constructing useful,
correct, compact and computationally sustainable
conceptualizations in the form of formal ontologies.
● Usually those who retain knowledge about a certain domain
(domain experts) are not experts in logic and are not
interested in becoming expert.
● Usually experts in logic (knowledge engineers) have
superficial and commonsense knowledge about a certain
domain.
● In ontology engineering domain experts and knowledge
engineers need to collaborate to build useful and correct
ontology based conceptualizations.

28. Collaborative Modelling
3rd Review Meeting, Graz

29. Collaborative Modeling…
June 3, 2009
3rd Review Meeting, Graz

30. …with dedicated supporting
tools

31. Two collaborative tools for ontology
engineering
● Moki = Modelling WiKi is a collaborative tool
that provides support for enabling domain
experts, who do not necessarily have
knowledge engineering skills, to model
business domains and simple processes
directly.
● Collaborative Protege is an extension of the
existing Protege system that supports
collaborative ontology editing as well as
annotation of both ontology components and
ontology changes.

32. Proposal for a modeling experience
● We constitute n modelling groups G(1) .... G(n)
● Mon­Tue
– G1..G(n/2) models moki producing models M1.. M(n/2)
– G(n/2+1)...Gn model with collaborative protege and
produce models M(n/2+1) … M(n)
● Wed­Thu
– G1..G(n/2) revse the models M(n/2+1)...M(n) in
collaborative protege
– G(n/2+1)...Gn revise the models M(1)...M(n/2) in moki
● Fri discussion on the experience and evaluation of the results

33. Domain model and task model for...
Technology enhanced learning
● The resulting model should allow to represent
– Classification of results, methodologies, scientific
articles, and tools in the area of TAL
– Construction of a semantic social network in which
people and organizations and activities are connected
by common/complementary interests
● The resulting model could be used for
– Searching for results, paper, people, projects, possible
collaborations
– Learning about TEL
– Keyword selection for semantic tagging