Categorization of the models based on structural information extraction and machine learning

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Springer Science and Business Media Deutschland GmbH

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Dergi sayısı


As various engineering fields increasingly use modelling techniques, the number of provided models, their size, and their structural complexity increase. This makes model management, including finding these models, with state of the art very expensive computationally, i.e., leads to non-tractable graph comparison algorithms. To handle this problem, modelers can organize available models to be reused and overcome the development of the new and more complex models with less cost and effort. Therefore, we utilized a model classification using baseline machine learning approaches on a dataset including 555 Ecore metamodels. In our proposed system, the structural information of each model was summarized in its elements through generating their simple labelled graphs. The proposed solution is to transform the complex attributed graphs of the models to simply labelled graphs so that graph analysis algorithms can be applied to them. The labelled graphs (models) were structurally compared using graph comparison techniques such as graph kernels, and the results were used as a set of features for similarity search. After generating feature vectors, the performance of six machine learning classifiers (Naïve Bayes (NB), k Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) were evaluated on the feature vectors. The presented model yields promising results for the model classification task with a classification accuracy over 87%.


Anahtar Kelimeler

Graph Kernel methods, Machine learning methods, Model management, Model transformation, Model-driven engineering, Graph grammars


Lecture Notes in Networks and Systems

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Khalilipour, A., Bozyiğit, F., Utku, C. & Challenger, M. (2022). Categorization of the models based on structural information extraction and machine learning. Paper presented at the Lecture Notes in Networks and Systems, Volume 505 LNNS, 173-181. doi:10.1007/978-3-031-09176-6_21