标题： 挖掘概念模型中的频繁结构 显示英文标题

标题： Mining Frequent Structures in Conceptual Models

摘要： 使用结构化方法来表示知识的问题在概念建模中是众所周知的，并且已经研究了很多年。 已经证明，采用建模模式是一种有效的结构化方法。 模式确实是可泛化的重复结构，可以作为解决设计问题的方案。 它们有助于理解和改进创建模型的过程。 在几个实验研究中，使用模式在概念建模中的不可否认的价值得到了证明。 然而，发现概念模型中的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模式识别解决方案。 在本文中，我们提出了一种解决发现频繁结构问题的通用方法，这些结构在概念建模语言中出现。 作为概念验证，我们通过专注于两种广泛使用的概念建模语言来实现我们的方法。 该实现包括一个探索性工具，该工具将频繁子图挖掘算法与图形操作技术集成在一起。 该工具处理多个概念模型，并根据各种标准识别重复结构。 我们使用两个最先进的精心整理的数据集对该工具进行了验证：一个由以OntoUML编码的模型组成，另一个由以ArchiMate编码的模型组成。 我们方法的主要目标是为语言工程师提供一个支持工具。 此工具可用于识别有效的和无效的建模实践，从而促进概念建模语言的精炼和演进。 此外，它促进了积累的专业知识的重用，最终支持在给定语言中创建更高质量的模型。 显示英文摘要

摘要： 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, we implement our approach by focusing on two widely-used conceptual modeling languages. This implementation includes an exploratory tool that integrates a frequent subgraph mining algorithm with graph manipulation techniques. The tool processes multiple conceptual models and identifies recurrent structures based on various criteria. We validate the tool using two state-of-the-art curated datasets: one consisting of models encoded in OntoUML and the other in ArchiMate. The primary objective of our approach is to provide a support tool for language engineers. This tool can be used to identify both effective and ineffective modeling practices, enabling the refinement and evolution of conceptual modeling languages. Furthermore, it facilitates the reuse of accumulated expertise, ultimately supporting the creation of higher-quality models in a given language.