Complexity in ontology-driven conceptual modeling

1 Introduction Since the late 1960s, the importance of conceptual modeling grew substantially due to the many information system project failures that were the consequence of faulty requirement analysis. Conceptual modeling was introduced as a means to enable early detection and correction of errors. Over the years, conceptual modeling has become a fundamental discipline in several subdomains of computer science. Conceptual modeling can be described as the activity of representing aspects of the physical and social world for the purpose of communication, learning and problem solving among human users [1]. Because of the importance attributed to conceptual modeling as a means to enable early detection and correction of errors, a wide range of conceptual modeling models and methods were developed and introduced. Criticism, however, arose stating that most of these modeling approaches and techniques were based on common sense and the intuition of their developers [2], therefore lacking sound theoretical foundations [3, 4]. Ontologies were introduced to provide a foundational theory that articulates and formalizes the conceptual modeling grammars needed to describe the structure and behavior of the modeled domain [5]. This led to the introduction of ontologies, which provide a foundation for conceptual modeling by means of a formal specification of the semantics of models and describe precisely which modeling constructs represent which phenomena [6]. Although ontologies were originally applied to analyze the constructs used in the models and evaluate conceptual grammars for their ontological expressiveness, the role of ontological theories evolved towards improving and extending conceptual modeling languages. These developments of enriching existing conceptual modeling languages with methodological guidelines that have their origin in a formalized ontology, is called ontology-driven conceptual modeling (ODCM). As mentioned by [7], many of these ontological theories such as those of Heller & Herre (2004), Chisholm (1996) and Bunge (1977) have been successfully applied. Both towards the evaluation of conceptual modeling languages (e.g., UML, ORM, ER, REA, OWL) and towards the development of structuring rules [8], modeling profiles (e.g. OntoUML) [9] and design patterns and anti-patterns [10]. Nonetheless the successful utilization of ontologies as theoretical foundations for conceptual models, the added value of their application however is not always straightforward. For example, some empirical evidence [11] has already confirmed that ontological rules can alleviate cognitive difficulties when developing conceptual models and that modelers commit fewer modeling errors when applying these ontological rules. However, [12] obtained less promising results. Their results agreed with those …