Semantically-aware Networks and Services for Training and Knowledge

Management in Organizations

Dr. Gilbert PaquetteDr. Gilbert Paquettewww.licef.ca/cice

Canada Research Chair in Instructional and Canada Research Chair in Instructional and Cognitive Enginerring (CICE)Cognitive Enginerring (CICE)

LICEF Research CenterLICEF Research Center

Télé-universitéTélé-université

NGNS’12 – Faro, Portugal – DecembrerNetworks and NGNS’12 – Faro, Portugal – DecembrerNetworks and ServicesServices

Software Developments at CICE/LICEF

MOT+LDMOT+LD

MOT +MOT +

MOT 2.0MOT 2.0

AGDAGD

MOT+OWLMOT+OWL

MISA 2.0MISA 2.0

MISA 4.0MISA 4.0

MISA LDMISA LD

ADISAADISA

MISA 3.0MISA 3.0

G-MOTG-MOT

PalomaPaloma

PalomaWebPalomaWeb

Competences +Competences +COMÈTECOMÈTE

TELOSScénario Ed.. Ontology Ed.

Competency Ed.Semantic Ref

Reccomenders

TELOSScénario Ed.. Ontology Ed.

Competency Ed.Semantic Ref

Reccomenders

Explor@Explor@

Concept@Concept@

Virtual CampusModel

Virtual CampusModel

Why Semantics ?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.

The Web of Data (Web 3.0)The Web of Data (Web 3.0)

URIs to identify all kinds of rssources Subject/relation/Object triples Graphs to relate Normalized syntax ( XML)

Web of documentsRelational DB

.

.

.

Web of linked dataRDF graphs

COMÈTECOMÈTE ArchitectureArchitecture

COMÈTE InterfaceCOMÈTE Interface

Semantic Question Semantic Question AnsweringAnswering

““Give me all the resources of a certain author?”Give me all the resources of a certain author?” ““Give me all the resources of an organization of a certain Give me all the resources of an organization of a certain

author?”author?” ““Give me all the resources from authors who have published Give me all the resources from authors who have published

with a certain list of authors?”with a certain list of authors?” ““Give me all the exercises references under “Atomic Give me all the exercises references under “Atomic

Physics” in the Dewey classification and under the Physics” in the Dewey classification and under the equivalent classifications in my University’s classifications?”equivalent classifications in my University’s classifications?”

““Give me all the Geometry tutorials , excluding Euclidian Give me all the Geometry tutorials , excluding Euclidian Geometry ?” Geometry ?”

““Give me all the Reports on open source tools that could Give me all the Reports on open source tools that could replace a certain tool ?””replace a certain tool ?””

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

The PRIOWS ProjectThe PRIOWS Project

Integrating data basesIntegrating data basesKnowledge ModelingKnowledge ModelingOntology ModelingOntology ModelingWork ScenarioWork ScenarioAssistanceAssistance

Ontology

Query

Experts

Documents

Data

Processes

Methods

Federated

Search

TELOSTELOS

Specialized TEL op. systemSpecialized TEL op. system Resource aggregation:Resource aggregation: ……in multi-actor scenarios in multi-actor scenarios Service-oriented system on NGNService-oriented system on NGN Ontology-driven systemOntology-driven system Produces semanticallly aware Web environmentProduces semanticallly aware Web environment

1010

LORNET (2003-2008):

A hundred researchers, A hundred researchers, assistants, graduate studentsassistants, graduate students

17 organizations, NSERC 17 organizations, NSERC support Semantic WEB support Semantic WEB researchresearch

TELOS

TELOS ArchitectureTELOS Architecture

Server

TechnicalOntologyTCP/IP

KBMan.

KBMan.

KBKB

Rel.BDRel.BD

Execution Semantic(based on the technical ontology)

Recommendation (assistance) Recommendation (assistance) PrinciplesPrinciples

Epiphyte – grafted on the scenario process Epiphyte – grafted on the scenario process

but external to it; no scenario modificationbut external to it; no scenario modification

Multi-agent system: agents are associated to Multi-agent system: agents are associated to

tasks at different levels in the scenariotasks at different levels in the scenario

Flexible association: one, some or all of the Flexible association: one, some or all of the

tasks are assisted.tasks are assisted.

Delegation between a task agent towards its Delegation between a task agent towards its

super tasks agents; tree topologysuper tasks agents; tree topology

InsertionInsertion of recommenders of recommenders (assistance agents): an example(assistance agents): an example

The implemented recommender The implemented recommender modelmodel

Recommender = {rules}Recommender = {rules} Rule = <targetActor, event, condition, action >Rule = <targetActor, event, condition, action > Event = Event =

– Activity transition Activity transition (started, terminated, revisited,…)(started, terminated, revisited,…)– Time spent (activity, global …) Time spent (activity, global …) – Resources opened, reopened,…Resources opened, reopened,…

Condition = boolean expression comparing: Condition = boolean expression comparing: – Target actor progress in the scenario + Target actor progress in the scenario + knowledge and knowledge and

competencies acquired + evidence => competencies acquired + evidence => User persistent modelUser persistent model

– Resources: prerequisite and target competenciesResources: prerequisite and target competencies

– Activities: prerequisite and target competenciesActivities: prerequisite and target competencies

Action = advice, notification, model updateAction = advice, notification, model update

Knowledge Descriptors

Classes and instances (From OWL-DL domain ontologies)General properties:

Domain – Data Properties / Domain – ObjectProperty – RangeInstanciated properties (facts):

Instance – Property / Instance – Property – Value

Competency Descriptors

(K, S, P) triples(K, S, P) triples

K: Knowledge descriptorK: Knowledge descriptor– From a OWL domain ontologyFrom a OWL domain ontology

S: Generic SkillS: Generic Skill– From a 10-level taxonomy From a 10-level taxonomy

(Paquette, 2007)(Paquette, 2007)

P: Performance levelP: Performance level– A combination of P-values A combination of P-values

(Paquette, 2007) (Paquette, 2007)

S=ApplyS=ApplyS=ApplyS=Apply

P=ExpertP=ExpertP=ExpertP=Expert

K=PlanetK=PlanetK=PlanetK=Planet

Referencing Process in the TELOS Implementation

OntologyOntologycontructioncontructionor importor import

… and/or competencies

ResourceResourceselectionselection1111 2222

SemanticSemanticReferencingReferencingOf resourcesOf resources

3333

Semantic Search Methods

Type of SearchType of Search Type of ResultType of Result

Simple Using key words from the ontology

AdvancedUsing knowledge and competency Using knowledge and competency boolean queryboolean query

Resource PairingUsing semantic comparison between queried ressource and other resources

→ → Rests on knowledge and competency comparisonRests on knowledge and competency comparison

Ressources with an Ressources with an exact matchexact match

Exact match ORExact match OR

Semanticallynear match

Semanticallynear match

Exact match ORExact match OR

Knowledge Comparison (K1 et K2)

Based on the Based on the structure of the ontology where the of the ontology where the knowledge descriptors are storedknowledge descriptors are stored

Compare the Compare the neighbourhoods of K1 and K2of K1 and K2

Possible resultsPossible results– K2 K2 near and more and more specialized / / general than K1 than K1

Competency Comparison

Based on knowledge Based on knowledge comparison ((KK))

Base on Base on the distance between skills’ levels (between skills’ levels (HH) ) and and performance levels distances(performance levels distances(PP))

Possible resultsPossible results C2C2 veryNear / Near C1 C1 C2C2 stronger / weaker than C1than C1 C2 more C2 more specialized / general than C1than C1

C1=(K1, S1, P1) et C2=(K2, S2, P2)C1=(K1, S1, P1) et C2=(K2, S2, P2)

Competency ComparisonCompetency Comparison

Competency comparison Competency comparison within rule conditionswithin 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

Recommendation exampleRecommendation example

Notification exampleNotification example

User model updateUser model update

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.

Future ResearchFuture 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.

Questions, Comments ?Questions, Comments ?

www.licef.ca/gp www.licef.ca/cice

www.cogigraph.com

NGNS’12 – Faro, Portugal – Decembrer 2, 2012NGNS’12 – Faro, Portugal – Decembrer 2, 20124th International Conference on Next Generation 4th International Conference on Next Generation

Networks and ServicesNetworks and Services