Using n-grams for the automated clustering of structural models

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Abstract

Model comparison and clustering are important for dealing with many models in data analysis and exploration, e.g. in domain model recovery or model repository management. Particularly in structural models, information is captured not only in model elements (e.g. in names and types) but also in the structural context, i.e. the relation of one element to the others. Some approaches involve a large number of models ignoring the structural context of model elements; others handle very few (typically two) models applying sophisticated structural techniques. In this paper we address both aspects and extend our previous work on model clustering based on vector space model, with a technique for incorporating structural context in the form of n-grams. We compare the n-gram accuracy on two datasets of Ecore metamodels in AtlanMod Zoo: small random samples using up to trigrams and a larger one (∼100 models) up to bigrams.

Original languageEnglish
Title of host publicationSOFSEM 2017: Theory and Practice of Computer Science - 43rd International Conference on Current Trends in Theory and Practice of Computer Science, Proceedings
PublisherSpringer
Pages510-524
Number of pages15
ISBN (Print)9783319519623
DOIs
Publication statusPublished - 2017
Event43rd Conference on Current Trends in Theory and Practice of Computer Science, (SOFSEM 2017), Januari 16-20, 2017, Limerick, Ireland - Limerick, Ireland
Duration: 16 Jan 201720 Jan 2017

Publication series

NameLecture Notes in Computer Science
Volume10139
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference43rd Conference on Current Trends in Theory and Practice of Computer Science, (SOFSEM 2017), Januari 16-20, 2017, Limerick, Ireland
CountryIreland
CityLimerick
Period16/01/1720/01/17

Keywords

  • Hierarchical clustering
  • Model comparison
  • Model-driven engineering
  • N-grams
  • Vector space model
  • n-grams

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