My presentation at NGNS-2012 Conference, Faro Portugal, 2 décembre 2012

1. NGNS ’ 12 – Faro, Portugal – DecembrerNetworks and
Services
Semantically-aware Networks and
Services for Training and Knowledge
Management in Organizations
Dr. Gilbert Paquette
www.licef.ca/cice
Canada Research Chair in Instructional and Cognitive
Enginerring (CICE)
LICEF Research Center
Télé-université

2. Software Developments
at CICE/LICEF
Virtual Campus
Virtual Campus
AGD
AGD Model
Model
MOT 2.0
MOT 2.0 MISA 2.0
MISA 2.0 Explor@
Explor@
MISA 3.0
MISA 3.0
ADISA
ADISA Paloma
Paloma
MOT
MOT ++
MISA 4.0
MISA 4.0 Concept@
Concept@
MISA LD
MISA LD
MOT+LD
MOT+LD MOT+OWL PalomaWeb
PalomaWeb
MOT+OWL
TELOS
TELOS
G-MOT
G-MOT Scénario Ed..
Scénario Ed..
COMÈTE
COMÈTE
Ontology Ed.
Ontology Ed.
Competency Ed.
Competency Ed.
Competences ++
Competences Semantic Ref
Semantic Ref
Reccomenders
Reccomenders

3. Why Semantics ?
1. Inform users (students, workers) during the execution of
task or learning activity of the content of the resources
that they use.
2. Assist users and designers in the selection of resources
appropriate to their knowledge and competencies.
3. Create well-balanced learning of work scenarios, locally
and globally.
4. Build user models for the personalization of learning or
work environments.
5. Provide an execution semantic for resources and
scenarios.

4. The Web of Data (Web 3.0)
Web of documents Web of linked data
Relational DB RDF graphs
.
.  URIs to identify all kinds of rssources
.  Subject/relation/Object triples
 Graphs to relate
 Normalized syntax ( XML)

5. COMÈTE
Architecture

6. COMÈTE Interface

7. Semantic Question
Answering
 “Give me all the resources of a certain author?”
 “Give me all the resources of an organization of a certain
author?”
 “Give me all the resources from authors who have
published with a certain list of authors?”
 “Give me all the exercises references under “Atomic
Physics” in the Dewey classification and under the
equivalent classifications in my University’s
classifications?”
 “Give me all the Geometry tutorials , excluding Euclidian
Geometry ?”
 “Give me all the Reports on open source tools that could
replace a certain tool ?””

8. The Adaptive Semantic Web
 Add semantic references to scenario
components: actors, tasks and resources within
educational modeling languages such as IMS-LD
(2003)
– Paquette and Marino, 2005
 “Include the improved modeling of users and
items, and contextual information into the
recommendation process”
– Adomavicus and Tuzhilin (2005)
 The “Adaptive Semantic Web” opens new
approaches for recommenders systems: use of
folksonomies and ontological filtering of resources
– Jannach et al, 2011

9. The PRIOWS Project
Data Documents
Processes
Experts
Integrating data bases
Knowledge Modeling Methods
Ontology
Ontology Modeling
Work Scenario
Query
Assistance
Federated
Search

10. TELOS
LORNET (2003-2008):
A hundred researchers,
assistants, graduate students
17 organizations, NSERC support
Semantic WEB research
TELOS
 Specialized TEL op. system
 Resource aggregation:
 …in multi-actor scenarios
 Service-oriented system on NGN
 Ontology-driven system
 Produces semanticallly aware Web environment
10

11. TELOS Architecture
Server
Technical KB
KB
Ontology
Man
Man KB
KB
TCP/IP ..
Rel.
Rel.
BD
BD

12. Execution Semantic
(based on the technical ontology)

13. Recommendation (assistance)
Principles
 Epiphyte – grafted on the scenario process
but external to it; no scenario modification
 Multi-agent system: agents are associated to
tasks at different levels in the scenario
 Flexible association: one, some or all of the
tasks are assisted.
 Delegation between a task agent towards its
super tasks agents; tree topology

14. Insertion of recommenders
(assistance agents): an example

15. The implemented recommender
model
 Recommender = {rules}
 Rule = <targetActor, event, condition, action >
 Event =
– Activity transition (started, terminated, revisited,…)
– Time spent (activity, global …)
– Resources opened, reopened,…
 Condition = boolean expression comparing:
– Target actor progress in the scenario + knowledge and
competencies acquired + evidence => User persistent model
– Resources: prerequisite and target competencies
– Activities: prerequisite and target competencies
 Action = advice, notification, model update

16. Knowledge Descriptors
Classes and instances (From OWL-DL domain ontologies)
General properties:
Domain – Data Properties / Domain – ObjectProperty – Range
Instanciated properties (facts):
Instance – Property / Instance – Property – Value

17. Competency Descriptors
(K, S, P) triples
 K: Knowledge descriptor K=Planet
K=Planet
– From a OWL domain
ontology
 S: S=Apply
S=Apply
Generic Skill
– From a 10-level taxonomy
(Paquette, 2007)
P=Expert
P=Expert
 P: Performance level
– A combination of P-values
(Paquette, 2007)

18. Referencing Process in the
TELOS Implementation
Ontology Resource Semantic
1
1 contruction 2
2 selection 3
3 Referencing
or import Of resources
… and/or
competencies

19. Semantic Search Methods
Type of Search Type of Result
Simple Ressources with an
Using key words from the ontology exact match
Advanced Exact match OR
Using knowledge and competency Semantically
boolean query near match
Exact match OR
Resource Pairing
Semantically
Using semantic comparison between
queried ressource and other resources near match
→ Rests on knowledge and competency comparison

20. Knowledge Comparison
(K1 et K2)
 Based on the structure of the ontology where the
knowledge descriptors are stored
 Compare the neighbourhoods of K1 and K2
 Possible results
– K2 near and more specialized / general than K1

21. Competency Comparison
C1=(K1, S1, P1) et C2=(K2, S2, P2)
 Based on knowledge comparison (K)
 Base on the distance between skills’ levels (H) and
performance levels distances(P)
 Possible results
 C2 veryNear / Near C1
 C2 stronger / weaker than C1
 C2 more specialized / general than C1

22. Competency Comparison

23. Competency comparison
within rule conditions
 A competency-based condition is a triple:
– ObjectCompetencyList is the list of prerequisite or target
competencies of another actor, a task or a resource to be
compared with user’s actual competency list
– Relation is one of the comparison relations : Identical, Near,
VeryNear, MoreGeneric, MoreSpecific, Stronger, Weaker, or
any combination of these.
– Quantification takes two values: HasOne or HasAll
 EX: HasAll /NearMoreSpecific / Target competencies for Essay
 EX: HasOne/Weaker/Target competency for Build Table activity

24. Recommendation example

25. Notification example

26. User model update

27. Achievements in PRIOWS
 Extension of the TELOS Technical Ontology for
semantic referencing of resources, search and
recommendation
 Definition of a Typology of semantic descriptors
(ontology descriptors and competenciers)
 Search methods for resources ‘identical’ ou ‘near’
sémantically
 Recommendation Model: based on competency
comparison between actors, tasks and resources
 New integrated suite of tools: Semantic referencer,
Semantic search tools, Competency and Ontology
editors, to Recommander Integration in scenarios,
Recomenders’ rule editor.

28. Future Research
 More experimental validation to refine the semantic relations
between OWL-DL references, i.e adding weights to the various
comparison cases
 Investigate recommendation methods for groups in
collaborative scenarios (permitted by our model of multi-actor
learning scenarios)
 Improve the practical use of the approach, partly automate
tasks, improve the ergonomics
 Investigate the integration of other recommendation methods
(e.g. user analytics)
 “Free” the suite of tools from TELOS to extend its usability on
the Web of data.

29. NGNS ’ 12 – Faro, Portugal – Decembrer 2, 2012
4th International Conference on Next Generation
Networks and Services
Questions, Comments ?
www.licef.ca/gp
www.licef.ca/cice
www.cogigraph.com