IDEAS home Printed from https://ideas.repec.org/a/igg/jswis0/v14y2018i1p1-30.html
   My bibliography  Save this article

Predicting Reasoner Performance on ABox Intensive OWL 2 EL Ontologies

Author

Listed:
  • Jeff Z. Pan

    (Department of Computing Science, University of Aberdeen, Aberdeen, UK)

  • Carlos Bobed

    (IRISA/Université de Rennes 1, Rennes, France)

  • Isa Guclu

    (University of Aberdeen, United Kingdom, Aberdeen, UK)

  • Fernando Bobillo

    (I3A, University of Zaragoza, Zaragoza, Spain)

  • Martin J. Kollingbaum

    (University of Aberdeen, Aberdeen, UK)

  • Eduardo Mena

    (I3A, University of Zaragoza, Zaragoza, Spain)

  • Yuan-Fang Li

    (Faculty of Information Technology, Monash University, Clayton, VIC, Australia)

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.

Suggested Citation

  • Jeff Z. Pan & Carlos Bobed & Isa Guclu & Fernando Bobillo & Martin J. Kollingbaum & Eduardo Mena & Yuan-Fang Li, 2018. "Predicting Reasoner Performance on ABox Intensive OWL 2 EL Ontologies," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(1), pages 1-30, January.
    • Handle: RePEc:igg:jswis0:v:14:y:2018:i:1:p:1-30
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSWIS.2018010101
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jswis0:v:14:y:2018:i:1:p:1-30. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.