Conceptual models are built to depict and analyze complex systems. They are made of concepts and relationships among these concepts. In a particular domain, conceptual models are helpful for different stakeholders to reach a clear and unified view of domain problems. However, the process of building conceptual models is time-consuming, tedious, and expertise required. To improve the efficiency of the building process, this paper proposes a configurable semantic-based (semi-) automatic conceptual model transformation methodology (SbACMT) that tries to reuse existing conceptual models to generate new models. SbACMT contains three parts: (i) a configurable semantic relatedness computing method building on the structured linguistic knowledge base “ConceptNet” (SRCM-CNet), (ii) a specific meta-model, which follows the Ecore standard, defines the rules of applying SRCM-CNet to different conceptual models to automatically detect transformation mappings, and (iii) a multistep matching and transformation process that employs SRCM-CNet. A case study is carried out to detail the working mechanism of SbACMT. Furthermore, through a systematically analysis of this case study, we validate the performance of SbACMT. We prove that SbACMT can support the automatic transformation process of conceptual models (e.g., class diagrams). The scalability of SbACMT can be improved by adapting the meta-model and predefined syntax transformation rules.

中文翻译：

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基于可配置语义的概念模型转换方法

建立概念模型来描述和分析复杂的系统。它们由概念和这些概念之间的关系组成。在特定领域中，概念模型有助于不同的利益相关者清晰，统一地查看领域问题。但是，构建概念模型的过程非常耗时，乏味且需要专业知识。为了提高构建过程的效率，本文提出了一种可配置的基于语义的（半）自动概念模型转换方法（SbACMT），该方法试图重用现有的概念模型以生成新模型。SbACMT包含三个部分：（i）建立在结构化语言知识库“ ConceptNet”（SRCM-CNet）之上的可配置语义相关性计算方法，（ii）遵循Ecore标准的特定元模型，定义了将SRCM-CNet应用于不同概念模型以自动检测转换映射的规则，以及（iii）采用SRCM-CNet的多步骤匹配和转换过程。进行了案例研究，详细介绍了SbACMT的工作机制。此外，通过对该案例研究的系统分析，我们验证了SbACMT的性能。我们证明SbACMT可以支持概念模型（例如，类图）的自动转换过程。通过调整元模型和预定义的语法转换规则，可以提高SbACMT的可伸缩性。此外，通过对该案例研究的系统分析，我们验证了SbACMT的性能。我们证明SbACMT可以支持概念模型（例如，类图）的自动转换过程。通过调整元模型和预定义的语法转换规则，可以提高SbACMT的可伸缩性。此外，通过对该案例研究的系统分析，我们验证了SbACMT的性能。我们证明SbACMT可以支持概念模型（例如，类图）的自动转换过程。通过调整元模型和预定义的语法转换规则，可以提高SbACMT的可伸缩性。