Semantic based model matching with emf compare
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The document presents a semantic-based model matching approach using the EMFCompare tool, designed to improve the accuracy of model comparisons by incorporating semantic relationships. It details various matching methods, challenges in model evolution, and proposed solutions using lexical resources like WordNet. The findings suggest that while extending EMFCompare with semantic capabilities enhances matching power, it may also introduce imprecision, highlighting the need for customized dictionaries based on the models being compared.
![ME‘16 – October 2, 2016 – Saint-Malo, France
10
The default match engine
The Levenshtein distance algorithm is applied on the
string representation of the elements
• For optimisation purposes the models are compared by considering
elements selected within a proper search window
...
foreach (elM1 : Model1.getElements())
foreach (elM2 : elM1.getWindowElements())
result[elM1][elM2] = calculateSimilarity(elM1, elM2)
return createMatches(result)
...](https://image.slidesharecdn.com/semantic-basedmodelmatchingwithemfcompare-161004095414/85/Semantic-based-model-matching-with-emf-compare-10-320.jpg)
Semantic based model matching with emf compare
- 1. Dipartimento di Ingegneria e Scienze Università degli Studi dell’Aquila dell’Informazione e Matematica Semantic-based Model Matching with EMFCompare Davide Di Ruscio davide.diruscio@univaq.it @ddiruscio Models and Evolution Workshop at MoDELS 2016 – October 2, 2016 – Saint-Malo, France
- 2. ME‘16 – October 2, 2016 – Saint-Malo, France 2 Joint work with Alfonso Pierantonio Unversity of L’Aquila (Italy) Ludovico Iovino Gran Sasso Science Institute (Italy) Juri Di Rocco Unversity of L’Aquila (Italy) Lorenzo Addazi Malardalen University (Sweden) Antonio Cicchetti Malardalen University (Sweden)
- 3. ME‘16 – October 2, 2016 – Saint-Malo, France 3 Introduction Model comparison is one of the most challenging operations in MDE It underpins a wide range of modelling activities • E.g., model versioning, evolution, collaborative modeling, … Calculating model differences relies on the model matching problem • It can be reduced to the problem of finding correspondences between two given graphs (Graph Isomorphism Problem, NP-Hard)
- 4. ME‘16 – October 2, 2016 – Saint-Malo, France 4 Introduction a b f c e d Version 1 a k l c e dVersion 2 m
- 5. ME‘16 – October 2, 2016 – Saint-Malo, France 5 Introduction a b f c e d Version 1 a k l c e dVersion 2 m Establish correspondences Calculate differences
- 6. ME‘16 – October 2, 2016 – Saint-Malo, France 6 Introduction a b f c e d Version 1 a k l c e dVersion 2 m Establish correspondences Calculate differences
- 7. ME‘16 – October 2, 2016 – Saint-Malo, France 7 Introduction a b f c e d Version 1 a k l c e dVersion 2 m Establish correspondences Calculate differences > Rename node b as k > Rename node f as l > Add node m > Add edge from k to m
- 8. ME‘16 – October 2, 2016 – Saint-Malo, France 8 Model-matching Static Identity-Based Matching: each model element has a persistent unique identifier that is assigned to it upon creation Signature-Based Matching: the identifier of each model element is dynamically calculated by combining the values of its features Similarity-Based Matching: models are typed attribute graphs and matching elements are identified by considering the aggregated similarity of their features. Language-Specific Matching: matching algorithms are tailored to a particular modelling language
- 9. ME‘16 – October 2, 2016 – Saint-Malo, France 9 Similiartiy-based matching Extensible • Static identity-based or signature-based matching can be also added by defining custom generator functions
- 10. ME‘16 – October 2, 2016 – Saint-Malo, France 10 The default match engine The Levenshtein distance algorithm is applied on the string representation of the elements • For optimisation purposes the models are compared by considering elements selected within a proper search window ... foreach (elM1 : Model1.getElements()) foreach (elM2 : elM1.getWindowElements()) result[elM1][elM2] = calculateSimilarity(elM1, elM2) return createMatches(result) ...
- 11. ME‘16 – October 2, 2016 – Saint-Malo, France 11 A meta-model evolution scenario A University theses management metamodel
- 12. ME‘16 – October 2, 2016 – Saint-Malo, France 12 A meta-model evolution scenario A University theses management metamodel Extract super class
- 13. ME‘16 – October 2, 2016 – Saint-Malo, France 13 A meta-model evolution scenario A University theses management metamodel Attribute renaming
- 14. ME‘16 – October 2, 2016 – Saint-Malo, France 14 A meta-model evolution scenario A University theses management metamodel
- 15. ME‘16 – October 2, 2016 – Saint-Malo, France 15 A meta-model evolution scenario A University theses management metamodel
- 16. ME‘16 – October 2, 2016 – Saint-Malo, France 16 A meta-model evolution scenario A University theses management metamodel
- 17. ME‘16 – October 2, 2016 – Saint-Malo, France 17 Contextual issues: limited consideration of the features characterising the elements surrounding/containing the compared one Linguistic issues: lack of semantical evaluation of the features characterizing the compared elements • False-negative e.g., renaming a given class using a syntactically different name • False-positive e.g., renaming a given class using a semantically different term, which however presents a strong syntactical similarity
- 18. ME‘16 – October 2, 2016 – Saint-Malo, France 19 Proposed approach Semantic Match Engine • Use of the WordNet lexical dictionary as ontological source
- 19. ME‘16 – October 2, 2016 – Saint-Malo, France 20 WordNet in a nutshell Lexical database for the English language English words are grouped into sets of synonyms (synsets) Each synset includes - a generic definition joining the contained words - semantic relationships connecting it to other synsets http://www.cs.princeton.edu/courses/archive/fall16/cos226/assignments/wordnet.html
- 20. ME‘16 – October 2, 2016 – Saint-Malo, France 21 WordNet in a nutshell Lexical database for the English language English words are grouped into sets of synonyms (synsets) Each synset includes - a generic definition joining the contained words - semantic relationships connecting it to other synsets http://www.cs.princeton.edu/courses/archive/fall16/cos226/assignments/wordnet.html
- 21. ME‘16 – October 2, 2016 – Saint-Malo, France 22 The proposed semantic model matching function createMatches(Comparison comparison, List leftEObjects, List rightEObjects){ SemanticMatch root = createSemanticMatch(null, null); exploreMatches(root, leftEObjects, rightEObjects); evaluateMatches(root); filterMatches(root, comparison); } Exploration Evaluation Filtering
- 22. ME‘16 – October 2, 2016 – Saint-Malo, France 23 The proposed semantic model matching function createMatches(Comparison comparison, List leftEObjects, List rightEObjects){ SemanticMatch root = createSemanticMatch(null, null); exploreMatches(root, leftEObjects, rightEObjects); evaluateMatches(root); filterMatches(root, comparison); } Exploration Evaluation Filtering A labelled graph representation of the compared models is produced • each node represents a semantic match • each incoming or outgoing labelled edge represents a connection with its parents or children elements
- 23. ME‘16 – October 2, 2016 – Saint-Malo, France 24 Type: EAttribute Source: Student.username Target: User.password Sim: null Type: EClass Source: Student Target: User Sim: null Type: EClass Source: Student Target: Student Sim: null Type: EAttribute Source: Student.password Target:User.password Sim: nullType: EAttribute Source: Student.password Target: User.username Sim: nullType: EAttribute Source: Student.username Target: User.username Sim: null
- 24. ME‘16 – October 2, 2016 – Saint-Malo, France 25 The proposed semantic model matching function createMatches(Comparison comparison, List leftEObjects, List rightEObjects){ SemanticMatch root = createSemanticMatch(null, null); exploreMatches(root, leftEObjects, rightEObjects); evaluateMatches(root); filterMatches(root, comparison); } Exploration Evaluation Filtering Each SemantichMatch node is integrated with the semantic distance value between the encapsulated element
- 25. ME‘16 – October 2, 2016 – Saint-Malo, France 26 Type: EAttribute Source: Student.username Target: User.password Sim: 0.2 Type: EClass Source: Student Target: User Sim: 0.4 Type: EClass Source: Student Target: Student Sim: 1 Type: EAttribute Source: Student.password Target:User.password Sim: 0.6Type: EAttribute Source: Student.password Target: User.username Sim: 0.2Type: EAttribute Source: Student.username Target: User.username Sim: 0.6
- 26. ME‘16 – October 2, 2016 – Saint-Malo, France 27 The proposed semantic model matching function createMatches(Comparison comparison, List leftEObjects, List rightEObjects){ SemanticMatch root = createSemanticMatch(null, null); exploreMatches(root, leftEObjects, rightEObjects); evaluateMatches(root); filterMatches(root, comparison); } Exploration Evaluation Filtering The set of SemanticMatch elements are filtered out with respect to a predefined threshold
- 27. ME‘16 – October 2, 2016 – Saint-Malo, France 28 Type: EAttribute Source: Student.username Target: User.password Sim: 0.2 Type: EClass Source: Student Target: User Sim: 0.4 Type: EClass Source: Student Target: Student Sim: 1 Type: EAttribute Source: Student.password Target:User.password Sim: 0.6Type: EAttribute ource: Student.password Target: User.username Sim: 0.2Type: EAttribute Source: Student.username Target: User.username Sim: 0.6
- 28. ME‘16 – October 2, 2016 – Saint-Malo, France 29 Experiments The Model Exchange Benchmark • 5 structural modelling languages • All the possible pairs of metamodels are given as input to: • Semantic EMFCompare • EMFCompare • GAMMA(*) • Coma++, FOAM, Crosi, Alignment API, AMW (*) M. Kessentini, A. Ouni, P. Langer, M. Wimmer, and S. Bechikh, “Search-based metamodel matching with structural and syntactic measures,” J. Syst. Softw., vol. 97, no. C, pp. 1–14, Oct. 2014.
- 29. ME‘16 – October 2, 2016 – Saint-Malo, France 30 Experiments Measures
- 30. ME‘16 – October 2, 2016 – Saint-Malo, France 31 Experiments Measures It denotes the percentage of correctly matched elements with respect to all the proposed matches
- 31. ME‘16 – October 2, 2016 – Saint-Malo, France 32 Experiments Measures It denotes the percentage of correctly matched elements with respect to all the expected matches
- 32. ME‘16 – October 2, 2016 – Saint-Malo, France 33 Experiments Measures It combines Precision and Recall to get an equally weighted average value of the measures
- 33. ME‘16 – October 2, 2016 – Saint-Malo, France 34 Experiments GAMMA provides best results with respect to Precision, Recall, and F-Measure GAMMA uses SBSE approaches and it requires to be initialized with a set of initial solutions (knowledge base)
- 34. ME‘16 – October 2, 2016 – Saint-Malo, France 35 Experiments Semantic EMFCompare: • produces more matches than expected • in some cases has lower Precision than EMFCompare • only in one case F-Measure is lower than EMFCompare
- 35. ME‘16 – October 2, 2016 – Saint-Malo, France 36 Experiments
- 36. ME‘16 – October 2, 2016 – Saint-Malo, France 37 Lessons learnt Extending EMFCompare with semantic aspects can be done in a lightweight manner An increasing matching power can come at the price of an increasing imprecision (more false-positives and false- negatives) The selection of the appropriate dictionary (depending on the artifacts to be compared) can make the difference • Comparing metamodels is semantically different than comparing models of specific domains Performing experiments can be an issue due to the lack of models to be used as test cases • Existing model mutations approaches should be extended to implement “semantics-aware” mutations
- 37. ME‘16 – October 2, 2016 – Saint-Malo, France 38 Conclusion and Future Work Model comparison is a very complex task It underpins the management of a wide number of (meta-)model (co-)evolution scenarios An extension of the EMFCompare tool has been proposed to enable “semantics-aware” matches Further experiments will be performed by considering the application of different dictionaries depending on the kinds of artifacts to be matched
- 38. ME‘16 – October 2, 2016 – Saint-Malo, France 39