Problem-based learning supported by semantic techniques2. 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
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.
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Problem-based learning supported by semantic techniques2. 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. 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
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
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
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
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
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
25. Terminology discrepancies
Missing and extra ontological elements
Reference model:
Learner model:
subclass of
missing term
extra term
equivalent terms
25
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
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
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