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