How can reasoner performance of ABox intensive ontologies be predicted?

Isa Guclu, Carlos Bobed, Jeff Z. Pan, Martin J. Kollingbaum, Yuan Fang Li

    Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

    2 Citations (Scopus)

    Abstract

    Reasoner performance prediction of ontologies in OWL 2 language has been studied so far from different dimensions. One key aspect of these studies has been the prediction of how much time a particular task for a given ontology will consume. Several approaches have adopted different machine learning techniques to predict time consumption of ontologies already. However, these studies focused on capturing general aspects of the ontologies (i.e., mainly the complexity of their TBoxes), while paying little attention to ABox intensive ontologies. To address this issue, in this paper, we propose to improve the representativeness of ontology metrics by developing new metrics which focus on the ABox features of ontologies. Our experiments show that the proposed metrics contribute to overall prediction accuracy for all ontologies in general without causing side-effects.

    Original languageEnglish
    Title of host publicationSemantic Technology
    Subtitle of host publication6th Joint International Conference, JIST 2016, Singapore, Singapore, November 2-4, 2016, Revised Selected Papers
    EditorsYuan-Fang Li, Wei Hu, Jin Song Dong, Grigoris Antoniou, Zhe Wang, Jun Sun, Yang Liu
    Place of PublicationCham, Switzerland
    PublisherSpringer
    Pages3-14
    Number of pages12
    ISBN (Electronic)9783319501123
    ISBN (Print)9783319501116
    DOIs
    Publication statusPublished - 2016
    EventJoint International Conference on Semantic Technology 2016 - Singapore, Singapore
    Duration: 2 Nov 20164 Nov 2016
    Conference number: 6th
    https://link-springer-com.ezproxy.lib.monash.edu.au/book/10.1007%2F978-3-319-50112-3 (Proceedings)

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume10055
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceJoint International Conference on Semantic Technology 2016
    Abbreviated titleJIST 2016
    Country/TerritorySingapore
    CitySingapore
    Period2/11/164/11/16
    Internet address

    Keywords

    • Semantic web
    • Ontology reasoning
    • Prediction
    • Random forests
    • Knowledge graph
    • Practical reasoning

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