The problem of using structured methods to represent knowledge is well-known in conceptual modeling and has been studied for many years. It has been proven that adopting modeling patterns represents an effective structural method. Patterns are, indeed, generalizable recurrent structures that can be exploited as solutions to design problems. They aid in understanding and improving the process of creating models. The undeniable value of using patterns in conceptual modeling was demonstrated in several experimental studies. However, discovering patterns in conceptual models is widely recognized as a highly complex task and a systematic solution to pattern identification is currently lacking. In this paper, we propose a general approach to the problem of discovering frequent structures, as they occur in conceptual modeling languages. As proof of concept for our scientific contribution, we provide an implementation of the approach, by focusing on UML class diagrams, in particular OntoUML models. This implementation comprises an exploratory tool, which, through the combination of a frequent subgraph mining algorithm and graph manipulation techniques, can process multiple conceptual models and discover recurrent structures according to multiple criteria. The primary objective is to offer a support facility for language engineers. This can be employed to leverage both good and bad modeling practices, to evolve and maintain the conceptual modeling language, and to promote the reuse of encoded experience in designing better models with the given language.

翻译：在概念建模中，使用结构化方法表示知识的问题广为人知，并已研究多年。事实证明，采用建模模式是一种有效的结构化方法。模式本质上是一种可泛化的递归结构，可作为设计问题的解决方案。它们有助于理解并改进模型创建过程。多项实验研究已证明在概念建模中使用模式的不可否认价值。然而，在概念模型中识别模式被广泛认为是一项高度复杂的任务，目前尚缺乏系统性的解决方案。本文针对概念建模语言中频繁结构发现的问题，提出了一种通用方法。作为科学贡献的概念验证，我们以UML类图（特别是OntoUML模型）为重点，实现了该方法。该实现包含一个探索性工具，通过结合频繁子图挖掘算法与图操作技术，能够处理多个概念模型并根据多重标准发现递归结构。主要目标是为语言工程师提供支持工具，可用于利用良好及不良建模实践、演进和维护概念建模语言，并促进在特定语言设计中重用编码经验以构建更优模型。