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Semantically-aware Networks and Services for Training and Knowledge Management in Organizations.


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

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Semantically-aware Networks and Services for Training and Knowledge Management in Organizations.

  1. 1. NGNS ’ 12 – Faro, Portugal – DecembrerNetworks and Services Semantically-aware Networks andServices for Training and Knowledge Management in Organizations Dr. Gilbert Paquette Research Chair in Instructional and Cognitive Enginerring (CICE) LICEF Research Center Télé-université
  2. 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. 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. 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. 5. COMÈTEArchitecture
  6. 6. COMÈTE Interface
  7. 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. 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. 9. The PRIOWS Project Data Documents Processes ExpertsIntegrating data basesKnowledge Modeling Methods OntologyOntology ModelingWork Scenario QueryAssistance Federated Search
  10. 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 environment10
  11. 11. TELOS Architecture Server Technical KB KB Ontology Man Man KB KBTCP/IP .. Rel. Rel. BD BD
  12. 12. Execution Semantic(based on the technical ontology)
  13. 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. 14. Insertion of recommenders(assistance agents): an example
  15. 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. 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. 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. 18. Referencing Process in the TELOS Implementation Ontology Resource Semantic11 contruction 2 2 selection 3 3 Referencing or import Of resources … and/or competencies
  19. 19. Semantic Search Methods Type of Search Type of ResultSimple Ressources with anUsing key words from the ontology exact matchAdvanced Exact match ORUsing knowledge and competency Semanticallyboolean query near match Exact match ORResource Pairing SemanticallyUsing semantic comparison betweenqueried ressource and other resources near match → Rests on knowledge and competency comparison
  20. 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. 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. 22. Competency Comparison
  23. 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. 24. Recommendation example
  25. 25. Notification example
  26. 26. User model update
  27. 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. 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. 29. NGNS ’ 12 – Faro, Portugal – Decembrer 2, 20124th International Conference on Next Generation Networks and ServicesQuestions, Comments ?