Measuring the Perceived Semantic Quality of Information Models

Semantic quality expresses the degree of correspondence between the information conveyed by a model and the domain that is modelled. As an early quality indicator of the system that implements the model, semantic quality must be evaluated before proceeding to implementation. Current evaluation approaches are based on ontological or meta-model analysis and/or use objective metrics. They ignore the model user's perception of semantic quality, which also determines whether the benefits of using a faithful model will be achieved. The paper presents the development of a perceived semantic quality measure. It presents a measure pre-test, i.e. a study aimed at refining and validating a new measure before its use in research and practice. The results of the pre-test show that our measure is reliable and that it is sufficiently differentiated from other perception-based measures of information model use like ease of use, usefulness, and user information satisfaction.