This document describes a system that supports problem-based learning through semantic techniques. The system uses semantic grounding to relate learner models to reference models. It analyzes discrepancies between models to generate semantic feedback for learners. This feedback covers terminology, taxonomy, qualitative reasoning structures, and suggestions agreed upon by multiple reference models. The system aims to help learners acquire domain knowledge and vocabulary through interaction with semantically-enabled models.

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
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7. Introduction
DynaLearn
7

8. QR Modelling
Entities
8

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

10. QR Modelling
Running simulations
10

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
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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

18. Semantic Grounding
18

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

42. Interface
OEG Oct 2010 42

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