Predicting reasoner performance on ABox intensive OWL 2 EL ontologies

Jeff Z. Pan, Carlos Bobed, Isa Guclu, Fernando Bobillo, Martin Kollingbaum, Eduardo Mena, Yuan Fang Li

    Research output: Contribution to journalArticleResearchpeer-review

    3 Citations (Scopus)

    Abstract

    In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.

    Original languageEnglish
    Pages (from-to)1-30
    Number of pages30
    JournalInternational Journal on Semantic Web and Information Systems
    Volume14
    Issue number1
    DOIs
    Publication statusPublished - 1 Jan 2018

    Keywords

    • ABox Reasoning
    • Machine Learning
    • Ontology
    • Performance Prediction
    • Semantic Web

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