Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
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This document discusses using semantic techniques to enable knowledge reuse in conceptual modeling. It describes how qualitative reasoning models can be semantically grounded and aligned using ontologies to provide ontology-based feedback. The techniques allow relating learner models to expert models to automate acquiring feedback and recommendations. An evaluation shows semantic web resources can ground domain vocabularies and ontology matching tools can align conceptual models. The approach supports creating semantically networked models to share and reuse conceptual knowledge.
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Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 1. Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling Jorge Gracia, Jochem Liem, Esther Lozano, Oscar Corcho, Michal Trna, Asunción Gómez-Pérez, and Bert Bredeweg Ontology Engineering Group, Universidad Politécnica de Madrid. Spain {jgracia, elozano, ocorcho, mtrna, asun}@fi.upm.es Informatics Institute. University of Amsterdam. The Netherlands {j.liem, b.bredeweg}@uva.nl ISWC, November 10th, Shanghai, China
- 2. Outline 2 1. Introduction 1. Qualitative Reasoning Modelling and Simulation 2. Application of Semantic Techniques 3. Existing approaches 2. Semantic Grounding 3. Ontology-Based Feedback 1. Types of feedback 2. Techniques of OBF 4. Some numbers 5. Conclusions 6. What’s going on... Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 3. Introduction • Conceptual representation of physical systems • Prediction of the system behaviour through reasoning • Simulation • Qualitative (important landmarks no numerical details) • Separation of structure and behaviour • Multiple domains of application • Environmental science • Physics • Economy • ... 3 Qualitative Reasoning Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 4. Introduction • Learning by modelling approach • Learners formally express and test their conceptual knowledge about systems in an educational context • Desirable features: – Shared learning environment, where expert and learner models are uploaded – Quality feedback extracted from the common knowledge 4 QR in science and education Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 5. QR Modelling and Simulation 5 Knowledge Representation Entity hierarchy Scenario Quantity: The dynamic aspects of the system Influence: Natality determines δSize Proportionality: δSize determines δNatality Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 6. Semantic Techniques • Bridging the gap between the loosely and imprecise terminology used by a learner and the well-defined semantics of an ontology • Relating the QR models created by other learners or experts in order to automate the acquisition of feedback and recommendations 6 How semantic techniques could help? Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 7. Semantic Techniques 7 DynaLearn http://hcs.science.uva.nl/projects/DynaLearn/ Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 8. Semantic Techniques 8 Student Model Grounding OWL export Semantic repository Quality feedback Online ontologies Modelling tool The Web Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 9. Existing approaches • QR modelling and simulation tools: Betty’s brain, Stella • Not grounding of terms to a common vocabulary • Not quality feedback from other models • Conceptual modelling techniques: CmapTools • Concepts maps for knowledge representation • Collaborative use • No use of Semantic Web to maximize interoperability • Not common shared vocabularies • Semantic techniques to enhance collaborative learning: DEPTH (Design Patterns Teaching Help System) • Focused on software engineering education • Supports recommendation more rather than quality feedback 9 Related work Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 10. Outline 10 1. Introduction 1. Qualitative Reasoning Modelling and Simulation 2. Application of Semantic Techniques 3. Existing approaches 2. Semantic Grounding 3. Ontology-Based Feedback 1. Types of feedback 2. Techniques of OBF 4. Some numbers 5. Conclusions 6. What’s going on... Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 11. Semantic Grounding 11 http://dbpedia.org/resource/Mortality_rate http://dbpedia.org/resource/Population http://www.anchorTerm.owl#NumberOf Expert/teacher Student grounding Semantic repository Anchor ontology Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 12. Semantic Grounding • 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 12 Benefits of grounding Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 13. Outline 13 1. Introduction 1. Qualitative Reasoning Modelling and Simulation 2. Application of Semantic Techniques 3. Existing approaches 2. Semantic Grounding 3. Ontology-Based Feedback 1. Types of feedback 2. Techniques of OBF 4. Some numbers 5. Conclusions 6. What’s going on... Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 14. Types of OBF • Ontology matching based feedback • Improvements of terminology • Missing and extra terms in the learner model − Missing hierarchical relations • Semantic reasoning based feedback: Inconsistency between hierarchies of models • Structure comparison based feedback: Differences between model structures 14Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 15. Modules of OBF 15 Ontology Matching Semantic Reasoner Missing/Extra Ontology Elements List of differences Structure Comparison Grounding-Based Alignment Learner Model + Reference Model Inconsistency between Hierarchies Differences between Model Structures Improvement of Terminology Preliminary mappings List of equivalent terms
- 16. Grounding-Based Alignment • In the learner model: • In the reference model: • Resulting preliminary mapping: 16
- 17. 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) 17 J. Gracia and E. Mena. Ontology matching with CIDER: Evaluation report for the OAEI 2008. In Proc. of 3rd Ontology Matching Workshop (OM’08), at ISWC’08, Karlsruhe, Germany, volume 431, pages 140-146. CEUR-WS, October 2008
- 18. Ontology Matching 18 Improvement of terminology Learner model: Reference model: equivalent terms with different label Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 19. Ontology Matching 19 Missing and extra terms in the learner model Learner model: Reference model: missing term extra term Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 20. Ontology Matching 20 Missing hierarchical relationships Reference model: missing term equivalent terms Learner model: subclass of Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 21. Semantic Reasoning 21 Inconsistency between hierarchies Reference model: Learner model: equivalent terms Disjoint classes INCONSISTENT HIERARCHIES! Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 22. Structure Comparison 22 Missing QR structures Learner model: Reference model: missing model structures Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 23. Outline 23 1. Introduction 1. Qualitative Reasoning Modelling and Simulation 2. Application of Semantic Techniques 3. Existing approaches 2. Semantic Grounding 3. Ontology-Based Feedback 1. Types of feedback 2. Techniques of OBF 4. Some numbers 5. Conclusions 6. What’s going on... Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 24. Experimental Evaluation Q1. Are Semantic Web resources suitable for grounding specific domain vocabularies? Q2. Are the state-of-the-art ontology matching tools suitable for the alignment of QR models? 24 Some Research Questions Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 25. Q1 – Semantic Web resources Knowledge source Coverage Ratio DBpedia 72% OpenCyc 69% WordNet 45% Watson 47% 25 Tested 1686 different English words coming from DynaLearn glossaries Knowledge source Coverage Ratio DBpedia + Yahoo Spelling Suggestion 78% What if we fix spelling errors and suggest nearby terms?: “fiter feeding” “filter feeding” Coverage Study What if we combine several sources?: Knowledge source Coverage Ratio DBpedia + OpenCyc 87% DBpedia + Watson 73% Dbpedia + WordNet 72% Dbpedia + OpenCyc + WordNet + Watson 88% Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 26. Q1 – Semantic Web resources • Tested 909 English labels covered by DBpedia, randomly selected from DynaLearn glossaries • Asked 8 expert evaluators, each one evaluated between 200-300 groundings. Each grounding was double-evaluated • Question: For each grounded term, are all the relevant meanings that you have in mind contained in the set of grounding candidates? If yes, mark the relevant ones. • Average accuracy: 83% 26 Accuracy Study Inter-evaluator Agreement Level Polysemy Agr eement (any) Cohen's Kappa 21,35 85% 0,47 Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 27. Q2 - Ontology Matching • Golden standard defined by experts • Eight QR models grouped by pairs • Semantic equivalences between them were identified • Result: reference alignment file • Separated ontology alignment: CIDER, Falcon • Each produced alignment was compared to the golden standard 27 Model Matching Experiment Precision Recall CIDER 92% 95% Falcon 67% 95% Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling W. Hu and Y. Qu. Falcon-ao: A practical ontology matching system. Journal of Web Semantics, 6(3):237-239, 2008
- 28. Outline 1. Introduction 1. Qualitative Reasoning Modelling and Simulation 2. Application of Semantic Techniques 3. Existing approaches 2. Semantic Grounding 3. Ontology-Based Feedback 1. Types of Feedback 2. Techniques of OBF 4. Some numbers 5. Conclusions 6. What’s going on... 28Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 29. Conclusions • Support the creation of semantically networked models to share and reuse conceptual knowledge • QR models are exported into an ontological language and grounded to an external common vocabulary • Ontology matching techniques used to get quality feedback 29 Conclusions Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 30. Outline 1. Introduction 1. Qualitative Reasoning Modelling and Simulation 2. Application of Semantic Techniques 3. Existing approaches 2. Semantic Grounding 3. Ontology-Based Feedback 1. Types of feedback 2. Techniques of OBF 4. Some numbers 5. Conclusions 6. What’s going on... 30Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 31. What’s going on… 31 Future Work • Run usability studies on our ontology-based feedback • Enrich our ontology matching based techniques with advanced metrics • Provide OBF results through the virtual characters • Model recommendation based on the community of users • Use our system in the academic domain to support semantic-guided learning • Publish this semantic data in the web of Linked Data Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
- 32. What’s going on… 32 OBF through virtual characters Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling Differences in terminology 2/8 The quantity Death rate of your model is equivalent to the quantity Mortality of the reference model. More info. Do you want to rename the term as Mortality? Yes No
- 33. Thanks for your attention! 33 Jorge Gracia Facultad de Informática Universidad Politécnica de Madrid Campus de Montegancedo sn 28660 Boadilla del Monte, Madrid http://www.oeg-upm.net jgracia@delicias.dia.fi.upm.es Phone: 34.91.3363670 Fax: 34.91.3524819 Some images under Creative Commons licence : http://www.flickr.com/photos/binkley27/2969227096/ http://www.flickr.com/photos/tauntingpanda/14782257/ http://www.flickr.com/photos/rainforest_harley/232636845/ Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
Notas do Editor
- QR models are conceptual model. Purpose is communication Conceptual models have an inherent ontology (particular perspective on the world) QR models are domain independent (all domains that can be described by system dynamics) Allows both scientific theories to be captured, but also thoughts of students QR models allow simulations. However they are qualitative in nature Qualitative means important values are made explicit Heavy metal concentration = {zero, positive, legal limit, illegal}. Its an abstraction from numerical values. QR models use a compositional modelling paradigm. That is, each distinct model part is represented as a model fragment. There is a strict separation of structure and behaviour. The structure describes the components of the system. The behaviour describes the dynamic aspects of the system. This separation allows the correct model fragments to be found that apply to a particular system. A simulation is based on an initial situation (scenario). The result of a simulation is a state graph. Each state represents a possible situation the system can be in. The transitions indicate how the system can change from one situation to another. Within DynaLearn domain experts are creating models to support an environmental science curriculum. There will be 100s of expert models in a semantic repository. Learners will also upload their models to the repository. We want to use this resource. Grounding makes this possible.
- Model = QR model as a type of conceptual model (not the same as a model in logics…)
- - Semantic Repository to storage the models and domain vocabularies - Grounding of terms to the common vocabulary - Ontology-based feedback on the quality of the models - Recommendation of models and model fragments based on model features or collaborative filtering