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 journalArticle

1 Citation (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

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Keywords

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

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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