A rigorous characterization of classification performance: A tale of four reasoners

Yong Bin Kang, Yuan Fang Li, Shonali Krishnaswamy

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    2 Citations (Scopus)

    Abstract

    A number of ontology reasoners have been developed for reasoning over highly expressive ontology languages such as OWL DL and OWL 2 DL. Such languages have, as a consequence of high expressivity, high worst-case complexity. Therefore, reasoning tasks such as classification sometimes take considerable time on large and complex ontologies. In this paper, we carry out a comprehensive comparative study to analyze classification performance of four widely-used reasoners, FaCT++, HermiT, Pellet and TrOWL, using a dataset of over 300 real-world ontologies. Our investigation on correlating reasoner performance with ontology metrics using machine learning techniques also provides additional insights into the hardness of individual ontologies.

    Original languageEnglish
    Title of host publicationProceedings of the OWL Reasoner Evaluation Workshop (ORE 2012)
    Subtitle of host publicationCollocated with IJCAR 2012 Conference, July 1st, Manchester,UK
    Editors Ian Horrocks, Mikalai Yatskevich, Ernesto Jimenez-Ruiz
    PublisherRheinisch-Westfaelische Technische Hochschule Aachen
    Number of pages12
    Publication statusPublished - 2012
    EventInternational Joint Conference on Automated Reasoning 2012 - Manchester, United Kingdom
    Duration: 1 Jul 20121 Jul 2012

    Publication series

    NameCEUR Workshop Proceedings
    PublisherRheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V
    Volume858
    ISSN (Electronic)1613-0073

    Conference

    ConferenceInternational Joint Conference on Automated Reasoning 2012
    Country/TerritoryUnited Kingdom
    CityManchester
    Period1/07/121/07/12

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