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
    Country/TerritoryIreland
    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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