Conceptual modeling is used to model application domains for which an information system is needed. One of the most complex domains to which conceptual modeling has been applied is that of the human genome. Due to its complexity, its understanding is often left to domain experts. Conceptual models represent genomics-related concepts, with various purposes, including domain clarification or data structures design for facilitating data integration. However, traditional conceptual models, which might be expressed, for example, with UML, may not be appropriate for properly explaining such a complex domain, thus requiring an additional layer to ground the model on well-accepted ontological foundations. To achieve this result, an “ontological unpacking” method has been proposed that uses OntoUML as a visual formalism. In this research, we carry out an empirical study to compare the two mentioned representations. The study involved a small group of participants, who responded to a set of questions by reading either a UML model or its related OntoUML unpacked version; the results enabled us to assess their understanding of the domain. We aim to initiate a practical evaluation framework to assess the effectiveness, efficiency and user beliefs of models derived by ontologically unpacking traditional conceptual models. The results of the analysis provide the basis for a broader assessment
