Ontological clarity and conceptual model validation: an experimental evaluation

Milton, Simon K., Rajapakse, Jayantha and Weber, Ron (2009). Ontological clarity and conceptual model validation: an experimental evaluation. In: 19th Workshop on Information Technologies and Systems. Proceedings. WITS 2009: 19th Workshop on Information Technologies and Evaluation, Phoenix, AZ, USA, (31-36). 14-15 December, 2009.

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Name Description MIMEType Size Downloads
Author Milton, Simon K.
Rajapakse, Jayantha
Weber, Ron
Title of paper Ontological clarity and conceptual model validation: an experimental evaluation
Conference name WITS 2009: 19th Workshop on Information Technologies and Evaluation
Conference location Phoenix, AZ, USA
Conference dates 14-15 December, 2009
Proceedings title 19th Workshop on Information Technologies and Systems. Proceedings
Place of Publication Phoenix, AZ, USA
Publisher Workshop on Information Technologies and Systems
Publication Year 2009
Sub-type Fully published paper
Open Access Status
Start page 31
End page 36
Total pages 6
Language eng
Formatted Abstract/Summary
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.
Keyword Data model validation
Experimental design
Ontological clarity
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Nominated for Best Paper Award

Document type: Conference Paper
Collection: UQ Business School Publications
 
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Created: Mon, 15 Dec 2014, 11:21:39 EST by Karen Morgan on behalf of UQ Business School