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Challenges of Identifying Communities with Shared Semantics in Enterprise Modeling

机译：在企业建模中以共享语义识别社区的挑战

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In this paper we discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling. People tend to understand modeling meta-concepts (i.e., a modeling language's constructs or types) in a certain way and can be grouped by this understanding. Having an insight into the typical communities and their composition (e.g., what kind of people constitute a semantic community) would make it easier to predict how a conceptual modeler with a certain background will generally understand the meta-concepts he uses, which is useful for e.g., validating model semantics and improving the efficiency of the modeling process itself. We demonstrate the use of psychometric data from two studies involving experienced (enterprise) modeling practitioners and computing science students to find such communities, discuss the challenge that arises in finding common real-world factors shared between their members to identify them by and conclude that the common (often implicit) grouping properties such as similar background, focus and modeling language are not supported by empirical data.
机译：在本文中，我们讨论了在企业建模中使用共享语义识别社区的用途和挑战。人们倾向于以某种方式理解建模元概念（即建模语言的构造或类型），并且可以通过这种理解进行分组。深入了解典型社区及其组成（例如，什么样的人构成语义社区）将使预测具有特定背景的概念建模者通常如何理解其使用的元概念变得更加容易。例如，验证模型语义并提高建模过程本身的效率。我们展示了来自两项研究的心理学数据的使用，该研究涉及经验丰富的（企业）建模从业人员和计算机科学专业的学生，​​以找到这样的社区，讨论了在寻找成员之间共享的共同现实因素以识别身份时所面临的挑战，并得出结论经验数据不支持常见的（通常是隐式的）分组属性，例如相似的背景，焦点和建模语言。

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