In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, domain experts must be able to understand and reason with their content. In other words, these models need to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages on the rich semantics of ontology-driven conceptual models (ODCM). In particular, we propose a formal notion of Relational Context to guide the automated clusterization (or modular breakdown) of conceptual models. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator). The paper also presents computational support for automating the identification of Relational Contexts and this modular breakdown procedure. Finally, we report the results of an empirical study assessing the cognitive effectiveness of this approach.

近年来，人们越来越关注使用参考概念模型来捕获有关复杂和敏感业务领域（例如，金融、医疗保健、空间）的信息。这些模型在不同类型的关键语义互操作性任务中发挥着重要作用。因此，领域专家必须能够理解和推理他们的内容。换句话说，这些模型需要在认知上易于处理。本文通过提出一种模型聚类技术来实现这一目标，该技术利用了本体驱动的概念模型 (ODCM) 的丰富语义。特别是，我们提出了一个正式的关系上下文概念指导概念模型的自动聚类（或模块化分解）。这种关系上下文捕获了在客观化（具体化）关系（关系者）范围内理解“角色扮演者”实体所需的所有信息。本文还提供了用于自动识别关系上下文和这种模块化分解过程的计算支持。最后，我们报告了一项评估这种方法的认知有效性的实证研究的结果。