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Problem-based learning supported
     by semantic techniques

        Esther Lozano, Jorge Gracia, Oscar Corcho


   Ontology Engineering Group, Universidad Politécnica de Madrid. Spain
                  {elozano,jgracia,ocorcho}@fi.upm.es
Outline


1. Introduction

2. System overview

3. Semantic grounding

4. Semantic-based feedback

5. Conclusions and Future Work




                         2
Introduction




“Engaging and informed tools for learning conceptual system knowledge”



                                   3
Introduction

Qualitative Reasoning
  • Tries to capture human
    interpretation of reality
  • Physical systems represented in
    models
  • System behaviour studied by
    simulation
  • Focused on qualitative variables
    rather than on numerical ones
    (eg., certain tree has a “big” size,
    certain species population
    “grows”, etc.)




                                     4
Introduction

Application: Learning of Environmental Sciences


• Core idea: “Learning by modelling”
• Learning tools:
   • Definition of a suitable terminology
   • Interaction with the model
   • Prediction of its behaviour
• Application examples:
   • “Study the evolution of a species
     population when another species is
     introduced in the same ecosystem”
   • “Study the effect of contaminant
     agents in a river”
   • ....


                                     5
Introduction

DynaLearn

• “System for knowledge acquisition of conceptual knowledge in the
  context of environmental science”. It combines:
   • Model construction representing a system
   • Semantic techniques to put such models in relationship
   • Use of virtual characters to interact with the system




                                  6
Introduction

DynaLearn




            7
QR Modelling

Entities




           8
QR Modelling

Model fragments


Entity: model fragment:
Imported
  Reuse structure of the
   The within a model      system
                                        Influence:
                                          Natality determines δSize

Quantity:
  The dynamic aspects of
  the system




Proportionality:
  δSize determines δNatality




                                    9
QR Modelling

Running simulations




                      10
QR Modelling

Simulations Results

• Based on a scenario,
  model fragments and
  model ingredient
  definitions
                                   State Graph




             Dependencies View of State 1         Value History
                                     11
Semantic Techniques

Semantic Techniques

     • To bridge the gap between the loosely and imprecise
       terminology used by a learner and the well-defined semantics
       of an ontology
     • To put in relation to the QR models created by other learners
       or experts in order to automate the acquisition of feedback and
       recommendations from others




                                  12
Outline


1. Introduction

2. System overview

3. Semantic grounding

4. Semantic-based feedback

5. Conclusions and Future Work




                         13
System overview




              Online semantic     Semantic repository
              resources



Learner            Grounding of       Grounded             Recommendation          Reference
 Model            learner model     Learner Model          of relevant models       Model




          ?

                                                                           Generation of
                                          List of suggestions
                                                                         semantic feedback
Learner




                                          14
Outline


1. Introduction

2. System overview

3. Semantic grounding

4. Semantic-based feedback

5. Conclusions and Future Work




                         15
Semantic Grounding

Expert/teacher                                                          Learner



                                                      Anchor ontology

                                       http://www.anchorTerm.owl#NumberOf
                 http://dbpedia.org/resource/Population
                 http://dbpedia.org/resource/Mortality_rate


                                   grounding




                              Semantic repository


                                       16
Semantic Grounding

Benefits of grounding

     • Support the process of learning a domain vocabulary
     • Ensure lexical and semantic correctness of terms
     • Ensure the interoperability among models
     • Extraction of a common domain knowledge
     • Detection of inconsistencies and contradictions between
       models
     • Inference of new, non declared, knowledge
     • Assist the model construction with feedback and
       recommendations




                                 17
Semantic Grounding




18
Outline


1. Introduction

2. System overview

3. Semantic grounding

4. Semantic-based feedback

5. Conclusions and Future Work




                         19
Semantic-based feedback


Learner
 Model        Grounding-based         Preliminary       Ontology
                 alignment             mappings         matching

Reference
 Model

                                                          List of
                                QR structures          equivalences
                                Discrepancies


               List of            Taxonomy            Generation of
            suggestions         Inconsistencies     semantic feedback



                                Terminology
                                Discrepancies
Grounding-based alignment

                    http://dbpedia.org/resource/Mortality_rate

Expert model
                                                                 Student model




                                   grounding




                           Semantic repository



               Preliminary mapping: Death_rate ≡ Death
Grounding-Based Alignment

• In the learner model:




• In the reference model:




• Resulting preliminary mapping:




                            22
Ontology Matching

• Ontology matching tool: CIDER
• Input of the ontology matching tool
        • Learner model with preliminary mappings
        • Reference model
• Output: set of mappings (Alignment API format)




Gracia, J. Integration and Disambiguation Techniqies for Semantic Heterogeneity Reduction on the Web. 2009

                                                                       23
Terminology discrepancies

Discrepancies between labels

 Learner model:                            Reference model:




                        equivalent terms with different label




                                  24
Terminology discrepancies

 Missing and extra ontological elements

                                            Reference model:
Learner model:




                                       subclass of




                                                               missing term
           extra term
                         equivalent terms




                                  25
Taxonomic discrepancies

Inconsistency between hierarchies

 Learner model:
                                     Reference model:
                                                        Disjoint classes




        INCONSISTENT
                                    equivalent terms
        HIERARCHIES!




                               26
QR structural discrepancies


      Algorithm:
               1. Extraction of basic units
               2.   Integration of basic units of the same type
               3.   Comparison of equivalent integrated basic units
               4.   Matching of basic units of the same type
               5.   Comparison of equivalent basic units




OEG Oct 2010                             27
QR structural discrepancies

  Extraction of basic units
                                                  External relationships




               Internal relationships

OEG Oct 2010                            28
QR structural discrepancies


      Algorithm:
               1. Extraction of basic units
               2. Integration of basic units of the same type
               3. Comparison of equivalent integrated basic units
               4. Matching of basic units of the same type
               5. Comparison of equivalent basic units




OEG Oct 2010                            29
QR structural discrepancies

  Integration of basic units by type




OEG Oct 2010                           30
QR structural discrepancies


      Algorithm:
               1. Extraction of basic units
               2. Integration of basic units of the same type
               3. Comparison of equivalent integrated basic units
                 1. Missing instances in the learner model
                 2. Discrepancies in the internal relationships
               4. Matching of basic units of the same type
               5. Comparison of equivalent basic units




OEG Oct 2010                               31
QR structural discrepancies

   Missing instances in the learner model

Reference model
                                                         Learner model




                Missing quantity




 OEG Oct 2010                      32
QR structural discrepancies

   Discrepancies between internal relationships


Reference model                                            Learner model




                    Different causal dependency




 OEG Oct 2010                        33
QR structural discrepancies


      Algorithm:
               1. Extraction of basic units
               2. Integration of basic units of the same type
               3. Comparison of equivalent integrated basic units
               4. Matching of basic units
                 •   Filter by MF (matching of MF first)
                 •   Matching based on the external relations
               5. Comparison of equivalent basic units




OEG Oct 2010                               34
QR structural discrepancies

     Matching of basic units
Reference model




                                                    Learner model




   OEG Oct 2010                35
QR structural discrepancies


      Algorithm:
               1.   Extraction of basic units
               2.   Integration of basic units of the same type
               3.   Comparison of equivalent integrated basic units
               4.   Matching of basic units of the same type
               5. Comparison of equivalent basic units
                  1. Missing entity instances
                  2. Discrepancies in external relationships




OEG Oct 2010                            36
QR structural discrepancies

  Missing entity instances
                                                          Learner model



                  Missing entity instances

Reference model




OEG Oct 2010                         37
QR structural discrepancies

    Discrepancies in the internal relationships
                                                                    Learner model



                  Different causal dependencies



Reference model




  OEG Oct 2010                                38
Feedback from the pool of models


      Algorithm:
               1. Get semantic-based feedback from each model
               2. For each generated suggestion, calculate
                  agreement among models
               3. Filter information with agreement < minimum
                  agreement
               4. Communicate information to the learner




OEG Oct 2010                         39
Feedback from the pool of models

   Example:




Learner model




 OEG Oct 2010      40
Feedback from the pool of models

  Example:



               67%                            25%


      75%


                     67%




OEG Oct 2010                  41
Interface




OEG Oct 2010   42
Problem-based learning supported
     by semantic techniques

        Esther Lozano, Jorge Gracia, Oscar Corcho


   Ontology Engineering Group, Universidad Politécnica de Madrid. Spain
                  {elozano,jgracia,ocorcho}@fi.upm.es

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DynaLearn: Problem-based learning supported by semantic techniques

  • 1. Problem-based learning supported by semantic techniques Esther Lozano, Jorge Gracia, Oscar Corcho Ontology Engineering Group, Universidad Politécnica de Madrid. Spain {elozano,jgracia,ocorcho}@fi.upm.es
  • 2. Outline 1. Introduction 2. System overview 3. Semantic grounding 4. Semantic-based feedback 5. Conclusions and Future Work 2
  • 3. Introduction “Engaging and informed tools for learning conceptual system knowledge” 3
  • 4. Introduction Qualitative Reasoning • Tries to capture human interpretation of reality • Physical systems represented in models • System behaviour studied by simulation • Focused on qualitative variables rather than on numerical ones (eg., certain tree has a “big” size, certain species population “grows”, etc.) 4
  • 5. Introduction Application: Learning of Environmental Sciences • Core idea: “Learning by modelling” • Learning tools: • Definition of a suitable terminology • Interaction with the model • Prediction of its behaviour • Application examples: • “Study the evolution of a species population when another species is introduced in the same ecosystem” • “Study the effect of contaminant agents in a river” • .... 5
  • 6. Introduction DynaLearn • “System for knowledge acquisition of conceptual knowledge in the context of environmental science”. It combines: • Model construction representing a system • Semantic techniques to put such models in relationship • Use of virtual characters to interact with the system 6
  • 9. QR Modelling Model fragments Entity: model fragment: Imported Reuse structure of the The within a model system Influence: Natality determines δSize Quantity: The dynamic aspects of the system Proportionality: δSize determines δNatality 9
  • 11. QR Modelling Simulations Results • Based on a scenario, model fragments and model ingredient definitions State Graph Dependencies View of State 1 Value History 11
  • 12. Semantic Techniques Semantic Techniques • To bridge the gap between the loosely and imprecise terminology used by a learner and the well-defined semantics of an ontology • To put in relation to the QR models created by other learners or experts in order to automate the acquisition of feedback and recommendations from others 12
  • 13. Outline 1. Introduction 2. System overview 3. Semantic grounding 4. Semantic-based feedback 5. Conclusions and Future Work 13
  • 14. System overview Online semantic Semantic repository resources Learner Grounding of Grounded Recommendation Reference Model learner model Learner Model of relevant models Model ? Generation of List of suggestions semantic feedback Learner 14
  • 15. Outline 1. Introduction 2. System overview 3. Semantic grounding 4. Semantic-based feedback 5. Conclusions and Future Work 15
  • 16. Semantic Grounding Expert/teacher Learner Anchor ontology http://www.anchorTerm.owl#NumberOf http://dbpedia.org/resource/Population http://dbpedia.org/resource/Mortality_rate grounding Semantic repository 16
  • 17. Semantic Grounding Benefits of grounding • Support the process of learning a domain vocabulary • Ensure lexical and semantic correctness of terms • Ensure the interoperability among models • Extraction of a common domain knowledge • Detection of inconsistencies and contradictions between models • Inference of new, non declared, knowledge • Assist the model construction with feedback and recommendations 17
  • 19. Outline 1. Introduction 2. System overview 3. Semantic grounding 4. Semantic-based feedback 5. Conclusions and Future Work 19
  • 20. Semantic-based feedback Learner Model Grounding-based Preliminary Ontology alignment mappings matching Reference Model List of QR structures equivalences Discrepancies List of Taxonomy Generation of suggestions Inconsistencies semantic feedback Terminology Discrepancies
  • 21. Grounding-based alignment http://dbpedia.org/resource/Mortality_rate Expert model Student model grounding Semantic repository Preliminary mapping: Death_rate ≡ Death
  • 22. Grounding-Based Alignment • In the learner model: • In the reference model: • Resulting preliminary mapping: 22
  • 23. Ontology Matching • Ontology matching tool: CIDER • Input of the ontology matching tool • Learner model with preliminary mappings • Reference model • Output: set of mappings (Alignment API format) Gracia, J. Integration and Disambiguation Techniqies for Semantic Heterogeneity Reduction on the Web. 2009 23
  • 24. Terminology discrepancies Discrepancies between labels Learner model: Reference model: equivalent terms with different label 24
  • 25. Terminology discrepancies Missing and extra ontological elements Reference model: Learner model: subclass of missing term extra term equivalent terms 25
  • 26. Taxonomic discrepancies Inconsistency between hierarchies Learner model: Reference model: Disjoint classes INCONSISTENT equivalent terms HIERARCHIES! 26
  • 27. QR structural discrepancies Algorithm: 1. Extraction of basic units 2. Integration of basic units of the same type 3. Comparison of equivalent integrated basic units 4. Matching of basic units of the same type 5. Comparison of equivalent basic units OEG Oct 2010 27
  • 28. QR structural discrepancies Extraction of basic units External relationships Internal relationships OEG Oct 2010 28
  • 29. QR structural discrepancies Algorithm: 1. Extraction of basic units 2. Integration of basic units of the same type 3. Comparison of equivalent integrated basic units 4. Matching of basic units of the same type 5. Comparison of equivalent basic units OEG Oct 2010 29
  • 30. QR structural discrepancies Integration of basic units by type OEG Oct 2010 30
  • 31. QR structural discrepancies Algorithm: 1. Extraction of basic units 2. Integration of basic units of the same type 3. Comparison of equivalent integrated basic units 1. Missing instances in the learner model 2. Discrepancies in the internal relationships 4. Matching of basic units of the same type 5. Comparison of equivalent basic units OEG Oct 2010 31
  • 32. QR structural discrepancies Missing instances in the learner model Reference model Learner model Missing quantity OEG Oct 2010 32
  • 33. QR structural discrepancies Discrepancies between internal relationships Reference model Learner model Different causal dependency OEG Oct 2010 33
  • 34. QR structural discrepancies Algorithm: 1. Extraction of basic units 2. Integration of basic units of the same type 3. Comparison of equivalent integrated basic units 4. Matching of basic units • Filter by MF (matching of MF first) • Matching based on the external relations 5. Comparison of equivalent basic units OEG Oct 2010 34
  • 35. QR structural discrepancies Matching of basic units Reference model Learner model OEG Oct 2010 35
  • 36. QR structural discrepancies Algorithm: 1. Extraction of basic units 2. Integration of basic units of the same type 3. Comparison of equivalent integrated basic units 4. Matching of basic units of the same type 5. Comparison of equivalent basic units 1. Missing entity instances 2. Discrepancies in external relationships OEG Oct 2010 36
  • 37. QR structural discrepancies Missing entity instances Learner model Missing entity instances Reference model OEG Oct 2010 37
  • 38. QR structural discrepancies Discrepancies in the internal relationships Learner model Different causal dependencies Reference model OEG Oct 2010 38
  • 39. Feedback from the pool of models Algorithm: 1. Get semantic-based feedback from each model 2. For each generated suggestion, calculate agreement among models 3. Filter information with agreement < minimum agreement 4. Communicate information to the learner OEG Oct 2010 39
  • 40. Feedback from the pool of models Example: Learner model OEG Oct 2010 40
  • 41. Feedback from the pool of models Example: 67% 25% 75% 67% OEG Oct 2010 41
  • 43. Problem-based learning supported by semantic techniques Esther Lozano, Jorge Gracia, Oscar Corcho Ontology Engineering Group, Universidad Politécnica de Madrid. Spain {elozano,jgracia,ocorcho}@fi.upm.es