Despite all the research efforts in the last decades, information integration is a problem yet to be solved in real organizations, especially when it involves semantic issues. A complete and precise shared representation of all the concepts involved in the integration tasks is required to prevent several kinds of problems, such as misinterpretation of a piece of information by different business stakeholders, inconsistent data integration procedures, and incorrect information exchange between applications. A key goal of any conceptual data model is to provide the best possible understanding of its subjacent domain. Ontologically well-founded conceptual models present themselves as a solution to represent a domain in a more correct and complete scheme. Current well-founded conceptual modeling representation languages, however, focus on the structural perspective. On the other side, there are several restrictions that influence the behavior of the concepts of the domain; these restrictions are frequently represented as business rules. Business rules contribute to restrict the semantics of the domain concepts and their relationships. However, structural and behavioral perspectives are usually represented in distinct and non-integrated artifacts. This separation prevents complete understanding of the underlying semantics of the domain. This work proposes preliminary ideas towards integrating these two domain perspectives in a semantically rich and complete conceptual data model, represented as an ontology. The result is a more complete conceptual model, which may be further used in more reliable information integration processes.