Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling

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

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

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


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


QR Modelling and Simulation

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Knowledge Representation

Entity hierarchy Scenario

Quantity: The dynamic aspects of the system

Influence: Natality determines δSize

Proportionality: δSize determines δNatality


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

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DynaLearn

http://hcs.science.uva.nl/projects/DynaLearn/


Semantic Techniques

8

Student

ModelGrounding

OWL exportSemantic repository

Quality feedback

Online ontologies

Modelling tool The Web


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


Outline

10

1. Introduction








4. Some numbers

5. Conclusions



Semantic Grounding

11

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

http://www.anchorTerm.owl#NumberOf

Expert/teacher Student

grounding

Semantic repository

Anchor ontology


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


Outline

13

1. Introduction








4. Some numbers

5. Conclusions



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

Modules of OBF

15

Ontology Matching

Semantic Reasoner

Missing/ExtraOntology 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

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)

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

Ontology Matching

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Improvement of terminology

Learner model: Reference model:

equivalent terms with different label


Ontology Matching

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Missing and extra terms in the learner model


missing termextra term


Ontology Matching

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Missing hierarchical relationships

Reference model:

missing term

equivalent terms

Learner model:

subclass of


Semantic Reasoning

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Inconsistency between hierarchies

Reference model:

Learner model:

equivalent terms

Disjoint classes

INCONSISTENT HIERARCHIES!


Structure Comparison

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Missing QR structures


missing model structures


Outline

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1. Introduction








4. Some numbers

5. Conclusions



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


Q1 – Semantic Web resources

Knowledge source Coverage RatioDBpedia 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%


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%

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Accuracy Study

Inter-evaluator Agreement LevelPolysemy Agr eement (any) Cohen's Kappa

21,35 85% 0,47


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

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Model Matching Experiment

Precision RecallCIDER 92% 95%Falcon 67% 95%


W. Hu and Y. Qu. Falcon-ao: A practical ontology matching system. Journal of Web Semantics, 6(3):237-239, 2008

Outline

1. Introduction






1. Types of Feedback


4. Some numbers

5. Conclusions



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

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Conclusions


Outline

1. Introduction








4. Some numbers

5. Conclusions



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


What’s going on…

32

OBF through virtual characters


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

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

[email protected]

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/


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

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