2012 01 20 (upm) emadrid ocorcho upm dynalearn tecnologias semanticas en contexto aprendizaje...

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

3

“Engaging and informed tools for learning conceptual system knowledge”

Introduction

• 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

Qualitative Reasoning

Introduction

• 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

Application: Learning of Environmental Sciences

Introduction

• “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

DynaLearn

Introduction

7

DynaLearn

QR Modelling

8

Entities

QR Modelling

9

Model fragments

Entity: The structure of the systemImported model fragment: Reuse within a model

Influence: Natality determines δSize

Quantity: The dynamic aspects of the system

Proportionality: δSize determines δNatality

QR Modelling

10

Running simulations

QR Modelling

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

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Simulations Results

Dependencies View of State 1 Value History

State Graph

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

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Semantic Techniques

Outline

1. Introduction

2. System overview




13

System overview

14

List of suggestions

Grounding of learner model

Recommendation of relevant models

Generation of semantic feedback

Semantic repositoryOnline semantic resources

Learner Model

Grounded Learner Model

Reference Model

Learner

?

Outline

1. Introduction

2. System overview




15

Semantic Grounding

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http://dbpedia.org/resource/Mortality_ratehttp://dbpedia.org/resource/Population

http://www.anchorTerm.owl#NumberOf

Expert/teacher Learner

grounding

Semantic repository

Anchor ontology

Semantic 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

Benefits of grounding

Semantic Grounding

18

Outline

1. Introduction

2. System overview




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Semantic-based feedback

List of suggestions

Ontology matching

Learner Model Grounding-based

alignment

Reference Model

Preliminary mappings

List of equivalences

Generation of semantic feedback

Terminology Discrepancies

Taxonomy Inconsistencies

QR structures Discrepancies

Grounding-based alignment

http://dbpedia.org/resource/Mortality_rate

Expert modelStudent model

grounding

Semantic repository

Preliminary mapping: Death_rate ≡ Death

Grounding-Based Alignment

• In the learner model:

• In the reference model:

• Resulting preliminary mapping:

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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)

23

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

Terminology discrepancies

24

Discrepancies between labels

Learner model: Reference model:

equivalent terms with different label

Terminology discrepancies

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Missing and extra ontological elements

Reference model:

missing term

Learner model:

equivalent termsextra term

subclass of

Taxonomic discrepancies

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Inconsistency between hierarchies

Reference model:

Learner model:

equivalent terms

Disjoint classes

INCONSISTENT HIERARCHIES!

QR structural discrepancies

27OEG Oct 2010

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


28OEG Oct 2010

External relationships

Internal relationships

Extraction of basic units


29OEG Oct 2010

Algorithm:

1. Extraction of basic units2. Integration of basic units of the same type





30OEG Oct 2010

Integration of basic units by type


31OEG Oct 2010

Algorithm:


2. Integration of basic units of the same type3. Comparison of equivalent integrated basic units

1. Missing instances in the learner model

2. Discrepancies in the internal relationships




32OEG Oct 2010

Missing instances in the learner model

Missing quantity

Reference modelLearner model


33OEG Oct 2010

Discrepancies between internal relationships

Different causal dependency

Reference model Learner model


34OEG Oct 2010

Algorithm:



3. Comparison of equivalent integrated basic units4. Matching of basic units

• Filter by MF (matching of MF first)• Matching based on the external relations



35OEG Oct 2010

Matching of basic units

equivalent

equivalent

Reference model

Learner model


36OEG Oct 2010

Algorithm:




4. Matching of basic units of the same type5. Comparison of equivalent basic units

1. Missing entity instances

2. Discrepancies in external relationships


37OEG Oct 2010

Missing entity instances

Missing entity instances

Reference model

Learner model


38OEG Oct 2010

Discrepancies in the internal relationships

Reference model

Learner model

Different causal dependencies

Feedback from the pool of models

39OEG Oct 2010

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


40OEG Oct 2010

Learner model

Example:


41OEG Oct 2010

Example:

25%

75%

67%

67%

Interface

42OEG Oct 2010

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

2012 01 20 (upm) emadrid ocorcho upm dynalearn tecnologias semanticas en contexto aprendizaje...

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