Ontological clarity and conceptual model validation: An experimental evaluation

Simon K. Milton, Jayantha Rajapakse, Ron Weber

    Research output: Contribution to conferencePaperpeer-review


    Data Modelers document their understanding of the users' work domain via conceptual models. Once a model has been developed, they ought to check that it has no defects. The literature has little guidance about strategies and tactics to improve the effectiveness of model validation. In this light, we propose a theory arguing that two factors have a major impact on the effectiveness of validation-namely, the (a) ontological clarity of the models prepared, and the (b) extent to which a validation method engages users more with the semantics of the domain represented by a model. We experimentally tested the theory in which we systematically varied the levels of these two factors. Forty-eight expert data-modelers participated in our experiment. Their task was to find defects in the model they were given. Our results showed that those who received the model that had greater ontological clarity on average detected more defects. We obtained no effect for the validation method that we predicted would engage participants more with the semantics of the domain represented by the model they had been given.

    Original languageEnglish
    Number of pages6
    Publication statusPublished - 1 Jan 2009
    EventWorkshop on Information Technologies and Systems (WITS 2009) - Phoenix, United States of America
    Duration: 14 Dec 200915 Dec 2009
    Conference number: 19th


    WorkshopWorkshop on Information Technologies and Systems (WITS 2009)
    Abbreviated titleWITS 2009
    Country/TerritoryUnited States of America


    • Data model validation
    • Experimental design
    • Ontological clarity

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